Hey PaperLedge learning crew, Ernis here, ready to dive into some fascinating AI research! Today, we're tackling a paper about how to make AI agents, specifically those powered by those super-smart Large Language Models – think ChatGPT on steroids – better at learning through trial and error. It's all about making them more efficient in the real world.
Now, imagine you're teaching a robot to navigate a maze. It could wander around randomly, bumping into walls until it eventually finds the cheese. That's like how some AI agents learn right now – super inefficient! What we want is an agent that explores intelligently, learns quickly, and doesn't waste a ton of time (or resources) in the process. This is where reinforcement learning comes in.
Reinforcement learning is all about training an agent to make decisions in an environment to maximize some sort of reward. It's like training a dog with treats – good behavior gets a reward, bad behavior doesn't. The goal is to teach the agent to make the best decisions to get the most rewards over time.
The problem? These Large Language Models (LLMs), while amazing at understanding and generating text, often struggle with exploration in reinforcement learning. They tend to get stuck in local optima, like a tourist who only visits the same popular landmarks every time. They need to be a bit more adventurous!
This paper highlights that many current LLM-based agents aren't great at exploring effectively. And, the classic reinforcement learning techniques that are good at exploration are difficult to implement directly within these natural language-based systems. That's a real bummer.
So, what's the solution? Instead of trying to trick the LLM into acting like a good reinforcement learning algorithm, the researchers decided to have the LLM explicitly implement one! They chose something called "Posterior Sampling for Reinforcement Learning," which is known for its data efficiency. Think of it like giving the LLM a detailed map and a compass instead of just letting it wander aimlessly.
Posterior sampling is a cool technique. Imagine you're trying to figure out the best restaurant in a new city. Instead of just picking one at random, you form a belief about how good each restaurant is, based on initial information (like online reviews). Then, you sample from those beliefs – maybe give the restaurant with the highest potential a try. After you eat, you update your beliefs based on your experience. Repeat! Posterior sampling formalizes this idea, allowing the agent to balance exploration (trying new things) and exploitation (sticking with what works).
"We illustrate how LLMs can be used to explicitly implement an existing RL algorithm...whose capacity for statistically-efficient exploration is already well-studied."
The researchers essentially taught the LLM to think like a smart explorer, using a proven method. And guess what? It worked! In their experiments, this LLM-powered, exploration-savvy agent performed significantly better on tasks that required careful exploration. They were able to show a system that can handle natural language and make decisions to improve its results. That is a big deal!
Why does this matter? Well, think about:
For developers: This research offers a practical way to build more effective AI agents that can learn from limited data.
For researchers: It demonstrates a novel approach to integrating LLMs with reinforcement learning, opening up new avenues for exploration.
For everyone: It brings us closer to having AI assistants that can truly learn and adapt to our needs, making them more helpful and efficient in various real-world scenarios.
This could have implications for customer service bots to complex decision making agents in robotics and beyond! This is a big deal!
This research raises some interesting questions for our PaperLedge discussion:
Could this approach be applied to other reinforcement learning algorithms besides posterior sampling? What would be the challenges?
How far can we push the capabilities of LLMs to act as explicit implementations of complex algorithms? Are there limitations to this approach?
Could this approach be vulnerable to biases present in the training data of the LLM? How can we mitigate those risks?
That's the scoop on this paper, learning crew! Hope it sparked some curiosity and gave you a taste of the exciting things happening at the intersection of LLMs and reinforcement learning. Until next time, keep exploring!Credit to Paper authors: Dilip Arumugam, Thomas L. Griffiths

Hey PaperLedge learning crew, Ernis here! Today, we're diving into some seriously cool research about how to make those super-smart Large Language Models, or LLMs – think of them as the brains behind chatbots and AI assistants – even smarter.
These LLMs are already pretty good at answering questions, but what if we could teach them to actually think out loud before giving an answer? Like showing their work in math class, right? Turns out, when they explain their reasoning step-by-step, they get the final answer correct way more often. That's the core idea behind "reasoning before answering."
Now, the challenge comes when we try to train these LLMs on conversations where there’s a back-and-forth, a multi-turn exchange. Imagine you're teaching a student. You ask a question, they give their reasoning, and then their answer. But you don't want to feed their reasoning back into the model as part of the next question. It's like saying, "Okay, you said this before, now what's the answer?" It just messes things up!
The problem is, the usual way these LLMs are trained involves processing the entire conversation in one go, a single "forward pass" as the researchers call it. This is super efficient. But when you have reasoning steps that need to be excluded from the next input, you can't do that anymore. It's like trying to bake a cake with all the ingredients at once when you need to add them one at a time, mixing in between.
So, what did these clever researchers come up with? They invented a trick! Imagine you have a photocopy machine, and you duplicate just the final answer of the LLM. This allows the system to process the entire multi-turn reasoning process in one go.
But here's the kicker: you don't want the LLM to "see" the reasoning when it's processing the subsequent turns. It's like giving a student the answer key before they try the problem. No good! So, they also designed a special "attention mask." Think of it as blinders that prevent the LLM from peeking at the reasoning when it shouldn't. It forces the LLM to focus on the relevant parts of the conversation for each turn.
"This new approach significantly reduces training time."
The result? Much faster and more efficient training on these complex, multi-turn reasoning datasets. This means we can build even smarter and more capable AI assistants much quicker!
So, why does this matter?
For developers: Faster training means less time and resources spent on building and improving LLMs.
For researchers: This opens up new avenues for exploring more complex reasoning tasks and conversational AI.
For everyone else: Better reasoning in LLMs translates to more helpful, accurate, and trustworthy AI assistants that can solve complex problems and provide better support.
This research has me thinking...
Could this technique be applied to other types of data, like code generation or creative writing?
How does the quality of the reasoning steps affect the final answer? Is there a way to train LLMs to generate better reasoning?
Let me know what you think of this paper in the comments! Until next time, keep learning, keep questioning, and keep exploring the amazing world of AI. This is Ernis, signing off from PaperLedge!Credit to Paper authors: Ritesh Goru, Shanay Mehta, Prateek Jain

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're checking out a survey paper all about a recent challenge focused on something super cool: event-based eye tracking. Now, I know that sounds a bit techy, but stick with me, it's easier than you think.
Think about how movies used to be filmed, frame by frame. Event cameras are different. Instead of taking pictures at fixed intervals, they only record when something changes in the scene. Imagine a super-efficient surveillance system that only records when there's movement, not constant footage of an empty room. That's the basic idea!
This research focuses on using these special cameras to track where our eyes are looking. The challenge, part of a big computer vision workshop called CVPR, asked teams to build algorithms that could pinpoint the center of our pupil just by processing the data from these event cameras. Why is this important? Well, think about all the tech that could benefit:
Virtual Reality (VR): Imagine your VR headset knowing exactly where you're looking, making the experience way more immersive.
Medical Diagnostics: Eye movement can tell doctors a lot about your health. This tech could lead to earlier and more accurate diagnoses.
Assistive Technology: Helping people with disabilities control devices or communicate using only their eye movements.
The survey we're looking at summarizes the best methods used by the top teams in the challenge. They looked at things like:
Accuracy: How well the algorithm predicts the pupil's center.
Model Size: How much computing power it needs – can it run on a phone or does it need a supercomputer?
Number of Operations: How efficient the algorithm is – does it get the job done quickly?
So, the researchers are essentially giving us a cheat sheet to understand the state-of-the-art in event-based eye tracking. They break down the innovative approaches, highlighting the strengths and weaknesses of each. They also discuss the hardware side of things, exploring what kind of event cameras are best suited for this task.
This isn't just for tech wizards! This research has real-world implications for a lot of us. For example, imagine a future where your car knows when you're getting drowsy just by tracking your eyes, preventing accidents. Or personalized learning experiences that adapt to your focus and engagement in real-time.
"Event-based cameras offer a fundamentally different way to capture visual information, opening up exciting possibilities for eye tracking and beyond."
The survey is a crucial step in advancing this field. By analyzing and comparing different approaches, the researchers are helping to identify the most promising directions for future research and development.
So, here are a couple of things I'm wondering about after reading this:
How far away are we from seeing this technology integrated into everyday devices like smartphones or smart glasses?
What are the ethical considerations surrounding the use of eye-tracking technology, especially in terms of privacy and data security?
Let me know what you think, PaperLedge crew. This is Ernis, signing off. Keep learning!Credit to Paper authors: Qinyu Chen, Chang Gao, Min Liu, Daniele Perrone, Yan Ru Pei, Zuowen Wang, Zhuo Zou, Shihang Tan, Tao Han, Guorui Lu, Zhen Xu, Junyuan Ding, Ziteng Wang, Zongwei Wu, Han Han, Yuliang Wu, Jinze Chen, Wei Zhai, Yang Cao, Zheng-jun Zha, Nuwan Bandara, Thivya Kandappu, Archan Misra, Xiaopeng Lin, Hongxiang Huang, Hongwei Ren, Bojun Cheng, Hoang M. Truong, Vinh-Thuan Ly, Huy G. Tran, Thuan-Phat Nguyen, Tram T. Doan

Hey PaperLedge crew, Ernis here! Get ready to dive into some fascinating research that could change how we approach mental health assessments. We're talking about using AI to conduct structured clinical interviews, specifically something called the MINI - the Mini International Neuropsychiatric Interview. Think of it like a super-organized, standardized way for doctors to figure out what's going on with a patient's mental health.
Now, the idea of automating this with AI isn't new, but there's a catch. Existing AI models, even the really powerful ones, often miss the mark when it comes to following the precise rules and logic of psychiatric diagnoses. It's like trying to bake a cake using a recipe written for a totally different dish! That's where this paper comes in. They've created something called MAGI, and it's a game changer.
MAGI is a framework that turns the MINI into an automatic, step-by-step process that a computer can follow. The secret? It uses a team of AI "agents" that work together like a well-oiled machine. Imagine it like this: you have a group of experts, each with a specific role, working together to get a complete picture of the patient's mental health.
First, we have the Navigation Agent. Think of it as the map reader, guiding the interview through the correct branching paths based on the patient's answers. The MINI is like a "choose your own adventure" book, and this agent makes sure we're always on the right page.
Next up, the Question Agent is the friendly face of the interview. It crafts questions that aren't just diagnostic probes but also show empathy and explain why the questions are being asked. It's like having a therapist in your pocket, gently guiding you through the process.
Then there's the Judgment Agent. This agent is like the fact-checker, carefully evaluating whether the patient's responses meet the specific criteria for each part of the MINI. Are their symptoms really aligning with the diagnostic criteria? This agent helps make that determination.
Finally, we have the Diagnosis Agent, which is the detective. It takes all the information gathered and creates a "PsyCoT" – a Psychometric Chain-of-Thought. This is essentially a detailed explanation of how the AI arrived at its conclusion, mapping the patient’s symptoms directly to the clinical criteria. Think of it like showing your work in a math problem.

So, what makes MAGI special? It's all about combining clinical rigor with the kind of conversational adaptability you'd expect from a real person. And crucially, it offers explainable reasoning. It's not just giving you an answer; it's showing you how it arrived at that answer.
The researchers tested MAGI on over 1,000 real people, covering conditions like depression, anxiety, and even suicidal thoughts. The results were impressive, showing that MAGI is a significant step forward in using AI for mental health assessments.
But why does this matter? Well, think about it. Mental healthcare can be expensive and difficult to access. MAGI could potentially help make these assessments more affordable and available to a wider range of people. For healthcare professionals, it could free up their time to focus on more complex cases. For researchers, it opens up new avenues for understanding mental health conditions.
"MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning."
Now, before we wrap up, let's consider some potential discussion points:
Could AI like MAGI eventually replace human clinicians in some aspects of mental health assessment? And what are the ethical implications of that?
How do we ensure that AI-driven assessments are culturally sensitive and don't perpetuate existing biases in mental healthcare?
What's the best way to build trust in these AI systems, both for patients and for healthcare professionals?
This research is a reminder of how AI can be a powerful tool for good, especially when it's designed with careful attention to detail and a focus on real-world impact. Keep those questions brewing, crew, and I'll catch you on the next PaperLedge!Credit to Paper authors: Guanqun Bi, Zhuang Chen, Zhoufu Liu, Hongkai Wang, Xiyao Xiao, Yuqiang Xie, Wen Zhang, Yongkang Huang, Yuxuan Chen, Libiao Peng, Yi Feng, Minlie Huang

Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool research! Today, we're unraveling a paper that tackles the fascinating world of creating images using AI, specifically, making that process way faster.
Think of it like this: imagine you're trying to draw a picture pixel by pixel, but instead of just slapping down a color, you're going through a super complicated, iterative process for each one. That's kind of how some existing AI models, called Masked Autoregressive Models, or MARs, generate images. They're really good at it, producing high-quality results, but they're slow. Like, watching-paint-dry slow.
The problem is that MAR models use something called a "diffusion head," which, in simple terms, means they gradually refine each pixel through a lot of steps. It's like slowly sculpting clay, constantly adding and removing bits until it's perfect. Great for detail, but terrible for speed.
Now, the researchers behind this paper said, "Enough is enough! There has to be a faster way!" And guess what? They found one! They created a new model called the Fast AutoRegressive model, or FAR. It's all about speed and efficiency.
Instead of that slow diffusion head, FAR uses what they call a "shortcut head." Think of it like taking a super-express train directly to your destination, bypassing all the local stops. FAR essentially predicts the final pixel value with fewer steps, making the whole image generation process much quicker. It's like drawing with confident, bold strokes instead of tentative little dabs.
"FAR achieves 2.3x faster inference than MAR while maintaining competitive FID and IS scores."
So, what does this mean in practice? Well, imagine you're a game developer who needs to quickly generate textures for a new level, or a designer who wants to explore lots of different image variations. FAR could be a game-changer, allowing you to create high-quality images in a fraction of the time. And for those of us who just like playing around with AI art generators, it means we can see our creations come to life much faster!
But here's the really clever part: FAR also works seamlessly with something called "causal Transformers." Now, Transformers are a type of neural network that's really good at understanding sequences, like words in a sentence. These researchers figured out how to extend these Transformers to work with continuous data like images, without having to change the underlying architecture. It’s like teaching an old dog new tricks, without having to rebuild the dog!
The result? A model that's not only faster but also maintains the high quality we expect from autoregressive models. The paper claims FAR is 2.3 times faster than MAR while still producing images with similar levels of detail and realism. They tested it using metrics called FID and IS scores, which are basically ways to measure how good an AI-generated image looks to a human.
Why does this matter?
For researchers: It opens up new avenues for exploring autoregressive models in image generation without the bottleneck of slow inference.
For developers: It provides a practical tool for quickly generating high-quality visual content.
For everyone: It makes AI image generation more accessible and efficient, potentially leading to new creative applications.
So, what are your thoughts, PaperLedge crew? Here are a couple of questions bouncing around in my head:
Could FAR be adapted to generate other types of continuous data, like audio or even video?
As these models get faster and more efficient, what ethical considerations do we need to be aware of regarding the potential misuse of AI-generated images?
Let me know what you think! Until next time, keep exploring the edge of the paper!Credit to Paper authors: Tiankai Hang, Jianmin Bao, Fangyun Wei, Dong Chen

Hey PaperLedge learning crew, Ernis here, ready to dive into another fascinating piece of research! Today, we’re tackling a paper that's all about giving us more control over what those super-smart AI language models are saying. Think of it like this: you’ve got a talented, but sometimes unfiltered, friend. You love their creativity, but sometimes they say things that are, well, not quite right for the situation. You need a way to gently nudge them towards saying things that are more appropriate, without stifling their brilliance, right?
That's essentially what this paper is trying to do with large language models (LLMs). These models, like the ones that power chatbots and write articles, are trained to predict the next word in a sequence. But, because of the way they are trained, they can sometimes generate text that is toxic, biased, or just plain off-topic. The problem is that these models are really good at predicting the next word, but not so good at thinking about the overall message or the "vibe" of the entire response. It’s like they're focused on individual brushstrokes instead of the entire painting.
Now, the existing solutions to this problem are a bit clunky. One approach is to completely retrain the language model for every new attribute you want to control – say, making it less toxic or more personalized. But that's incredibly expensive and time-consuming. Imagine having to completely rebuild your friend's personality every time you want them to be more polite at a dinner party! Another approach involves trying to guess how the model's future words will impact the overall attribute, but that's slow and unreliable, especially for attributes that are rare or unusual.
Retraining: Expensive and inflexible.
Guessing (EAP Approximation): Slow and unreliable.

That's where this paper comes in with a brilliant new framework called TRACE, which stands for "Tractable Probabilistic Reasoning for Adaptable Controllable gEneration." Now, don’t let the name scare you! The key word here is "tractable," meaning manageable. TRACE offers a way to efficiently figure out how likely a language model is to produce text that fits a specific attribute, like being non-toxic or personalized. It’s like giving your friend a subtle reminder about the importance of being polite before they say something regrettable.
So, how does it work? The researchers cleverly distill the complex language model into a simpler representation called a Hidden Markov Model (HMM). Think of an HMM as a simplified map of the language model's brain, showing the most likely paths it will take when generating text. They then pair this HMM with a small classifier that's specifically trained to identify whether a piece of text has the desired attribute. This allows TRACE to quickly and accurately estimate the "Expected Attribute Probability" (EAP) of future sequences. In essence, it allows TRACE to "look ahead" and anticipate potential problems before they happen.
Finally, TRACE uses this EAP to tweak the language model's next-token probabilities, gently guiding it towards generating text that is more likely to have the desired attribute. It’s like giving your friend a nudge in the right direction, without completely dictating what they say.
"TRACE distills a Hidden Markov Model (HMM) from an LM and pairs it with a small classifier to estimate attribute probabilities, enabling exact EAP computation over the HMM's predicted futures."

The results are pretty impressive. The researchers found that TRACE achieved state-of-the-art results in detoxification – making language models less toxic – with only a tiny bit of extra processing time (about 10% overhead). They also showed that TRACE can be quickly adapted to personalize language models for different users or topics, and even handle combinations of attributes. Imagine being able to fine-tune a language model to be both non-toxic and personalized to your specific interests, all in a matter of seconds!
Detoxification: State-of-the-art results with minimal overhead.
Personalization: Adapts to new attributes in seconds.
Composite Attributes: Seamlessly handles combinations of attributes.

So, why does this research matter? Well, for anyone who's concerned about the potential harms of AI, TRACE offers a promising way to make language models safer and more aligned with human values. For developers, it provides a powerful and flexible tool for controlling the output of their models, without the need for expensive retraining. And for all of us, it means that AI-powered tools are becoming more responsible and trustworthy.
Here are some things to consider as we unpack this on the show:
How might TRACE be used to address other challenges in AI, such as reducing bias or improving factual accuracy?
Could this approach be applied to other types of AI models, beyond language models?
What are the potential ethical implications of having so much control over the output of AI systems?
That's all for this sneak peek, learning crew! I'm looking forward to diving deeper into this paper and discussing its implications with you all on the PaperLedge podcast. Stay curious!Credit to Paper authors: Gwen Yidou Weng, Benjie Wang, Guy Van den Broeck

Hey Learning Crew, Ernis here, ready to dive into some fascinating research that's all about teaching robots to learn by watching! Think of it like this: you want to teach a robot to make a perfect cup of coffee. You show it tons of videos of expert baristas, right? That's imitation learning in a nutshell.
Now, this paper tackles a big problem: generalization. It's like teaching your robot to make coffee only in your kitchen. What happens when it encounters a different coffee machine, or a different type of milk? It needs to generalize its skills to new situations.
The researchers looked at why robots trained on limited data often struggle to adapt. They used some pretty cool mathematical tools – specifically, information theory and a deep dive into data distribution – to figure out what's going on under the hood.
So, what did they find? Well, imagine the robot's brain as a complex network. The researchers discovered that the robot's ability to generalize depends on two main things:

Information Bottleneck: Think of this as a filter. The robot needs to filter out the unnecessary information from the videos and focus on the essential steps for making coffee. Too much noise, and it gets confused. This paper argues that a tighter "bottleneck" can sometimes lead to better generalization.


Model's Memory of Training: The robot shouldn't memorize every single detail of every video. It should learn the underlying principles. The less the robot remembers the specific training examples, the better it can adapt to new situations.

Here's where it gets really interesting. The paper offers guidance on how to train these robots effectively, especially when using those big, powerful "pretrained encoders" – like the language models that power AI chatbots but for robots! Should we freeze them, fine-tune them, or train them from scratch? The answer, according to this research, depends on those two factors we just talked about: the information bottleneck and the model's memory.
They also found that variability in the actions the robot takes is super important. It's not enough to just show the robot lots of different videos of people making coffee. You also need to show the robot how to recover from mistakes or use different techniques to achieve the same goal. The more ways the robot knows how to make coffee, the better it can handle unexpected situations.
...imitation learning often exhibits limited generalization and underscore the importance of not only scaling the diversity of input data but also enriching the variability of output labels conditioned on the same input.
Think about learning to ride a bike. You don't just watch videos, you try to ride the bike, you fall, you adjust, you learn from your mistakes. It's the same for robots!
So, why does this matter? Well, for:

Robotics Engineers: This research provides concrete guidelines for training robots that are more adaptable and reliable.


AI Researchers: It sheds light on the fundamental challenges of generalization in imitation learning and provides a theoretical framework for developing new training techniques.


Everyone Else: As robots become more integrated into our lives, understanding how they learn and adapt is crucial. This research helps us build robots that can handle the complexities of the real world.

This research really highlights the importance of diversity and variability in training data. Not just showing the robot a lot of different things, but a lot of different ways to do the same thing. This could influence future research in robotics. And one interesting note is that high conditional entropy from input to output has a flatter likelihood landscape. Interesting, right?
Here are a couple of things that are bubbling up for me:

Could this research help us design robots that are better at learning from limited data, which is often the case in real-world scenarios?


How can we automatically generate more diverse and variable training data for robots, without relying on human experts?

What do you think, Learning Crew? Let's discuss!Credit to Paper authors: Yixiao Wang

Alright learning crew, get ready to dive into the fascinating world of online recommendations! Today, we're unpacking a research paper focused on making those "you might also like" suggestions way better.
Think about it: whenever you're browsing your favorite online store or streaming platform, there's a whole system working behind the scenes to predict what you're most likely to click on. That's what we call click-through rate (CTR) prediction. It's basically a crystal ball for online behavior!
Now, these systems don't just guess randomly. They use all sorts of information – text descriptions, images, even your past browsing history – to understand what you're into. This is where the "multimodal" part comes in. It's like having different senses – sight, sound, touch – all contributing to a single understanding.
The trick is, this wealth of information can be overwhelming. Imagine trying to make a split-second decision with a million things flashing through your mind! That's the challenge these researchers are tackling: how to use all this "multimodal" data effectively, without slowing down the system. Because nobody wants to wait forever for a recommendation to load, right?
This paper actually stems from a competition – a "Multimodal CTR Prediction Challenge" – where researchers were given two main tasks. Task 1 was all about creating super-informative item embeddings, basically, really good digital representations of products using all the available information about them. Think of it like creating a detailed profile for each item so the system really understands what it is.
Task 2, and the focus of this paper, was about building a model that could actually use those embeddings to predict CTR. In other words, how can we use all this multimodal information to make the best possible predictions about what someone will click on?
The researchers came up with a model they call the "Quadratic Interest Network," or QIN for short. It's like a super-smart detective that uses two key techniques:
Adaptive Sparse Target Attention: This is a fancy way of saying the model focuses on the most important parts of your past behavior. Imagine you're shopping for a gift. The model might pay extra attention to the types of gifts you've searched for before, rather than every single thing you've ever looked at. It's like filtering out the noise and focusing on the signal.
Quadratic Neural Networks: These help the model understand complex relationships between different features. It's not just about liking cats or liking sweaters; it's about how much you like cat-themed sweaters! These networks can capture those high-order interactions.
Think of it like this: QIN is trying to understand not just what you like, but why you like it, and how different aspects of your preferences combine to influence your choices.
And the results? Impressive! The QIN model achieved a score of 0.9798 in AUC (Area Under the Curve), which is a common way to measure the accuracy of prediction models. This placed them second in the competition! That's like winning a silver medal at the Olympics of recommendation systems!
The best part? They've made their code, training logs, and everything else available online (at https://github.com/salmon1802/QIN) so other researchers can build on their work. That's what we call open science in action!
So, why does this matter? Well, for one thing, better recommendations mean a better online experience for everyone. We're more likely to find things we actually want, and less likely to waste time sifting through irrelevant suggestions.
But it's also important for businesses. More accurate CTR prediction can lead to increased sales and customer satisfaction. And for researchers, this work provides valuable insights into how to effectively use multimodal data in machine learning.
Here are a couple of things I'm wondering about as I chew on this research:
Could this model be adapted to predict other things besides clicks, like whether someone will watch a video or add something to their cart?
What are the ethical implications of using such sophisticated models to predict our behavior? Are we sacrificing privacy for convenience?
I'd love to hear your thoughts, learning crew! What are your takeaways from this paper? And what other questions does it spark for you?Credit to Paper authors: Honghao Li, Hanwei Li, Jing Zhang, Yi Zhang, Ziniu Yu, Lei Sang, Yiwen Zhang