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Sample Blog Post
Your blog post's abstract. Please add your abstract or summary here and not in the main body of your text. Do not include math/latex or hyperlinks.
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Sample Blog Post (HTML version)
Your blog post's abstract. Please add your abstract or summary here and not in the main body of your text. Do not include math/latex or hyperlinks.
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The Hidden Convex Optimization Landscape of Two-Layer ReLU Networks
In this article, we delve into the research paper titled 'The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks'. We put our focus on the significance of this study and evaluate its relevance in the current landscape of the theory of machine learning. This paper describes how solving a convex problem can directly give the solution to the highly non-convex problem that is optimizing a two-layer ReLU Network. After giving some intuition on the proof through a few examples, we will observe the limits of this model as we might not yet be able to throw away the non-convex problem.
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The N Implementation Details of RLHF with PPO
Reinforcement Learning from Human Feedback (RLHF) is pivotal in the modern application of language modeling, as exemplified by ChatGPT. This blog post delves into an in-depth exploration of RLHF, attempting to reproduce the results from OpenAI's inaugural RLHF paper, published in 2019. Our detailed examination provides valuable insights into the implementation details of RLHF, which often go unnoticed.
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Towards Robust Foundation Models: Adversarial Contrastive Learning
Foundation models pre-trained on large-scale unlabelled datasets using self-supervision can be generalizable to a wide range of downstream tasks. Existing work has shown that adversarial attacks can effectively fool any downstream models fine-tuned from a pre-trained foundation model. The existence of such adversarial attacks necessitates the development of robust foundation models which can yield both standard generalization and adversarial robustness to safety-critical downstream tasks. Currently, adversarial contrastive learning (ACL) is one of the most effective methods for outputting a robust foundation model. ACL incorporates contrastive learning with adversarial data to effectively output a robust representation without requiring costly annotations. In this blog, we introduced two NeurIPS 2023 publications that can enhance ACL's efficacy and efficiency, respectively. (1) This blog introduces Adversarial Invariant Regularization (AIR) which is a state-of-the-art ACL algorithm. A causal theoretical framework is built to interpret ACL, and then the AIR algorithm is derived from the causal framework to regulate and improve the ACL. (2) This blog also introduces a Robustness-aware Coreset Selection (RCS) method to speed up ACL. RCS does not require label information and searches for an informative training subset that can maintain the adversarial robustness. For the first time, RCS enables the application of ACL on the large-scale ImageNet-1K dataset.
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Understanding gradient inversion attacks from the prior knowledge perspective
In this blogpost, we mention multiple works in gradient inversion attacks, point out the chanllenges we need to solve in GIAs, and provide a perspective from the prior knowledge to understand the logic behind recent papers.
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Understanding in-context learning in transformers
We propose a technical exploration of In-Context Learning (ICL) for linear regression tasks in transformer architectures. Focusing on the article Transformers Learn In-Context by Gradient Descent by J. von Oswald et al., published in ICML 2023 last year, we provide detailed explanations and illustrations of the mechanisms involved. We also contribute novel analyses on ICL, discuss recent developments and we point to open questions in this area of research.
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What exactly has TabPFN learned to do?
TabPFN [Hollmann et al., 2023], a Transformer model pretrained to perform in-context learning on fresh tabular classification problems, was presented at the last ICLR conference. To better understand its behavior, we treat it as a black-box function approximator generator and observe its generated function approximations on a varied selection of training datasets. Exploring its learned inductive biases in this manner, we observe behavior that is at turns either brilliant or baffling. We conclude this post with thoughts on how these results might inform the development, evaluation, and application of prior-data fitted networks (PFNs) in the future.