Unlearnable Algorithms for In-context Learning
The paper ‘Unlearnable Algorithms for In-context Learning’ focuses on the concept of machine unlearning, a process that…
Building Expressive and Tractable Probabilistic Generative Models: A Review
This article presents a comprehensive survey of the advancements and techniques in the field of tractable probabilistic…
Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems
The paper proposes using techniques from control theory to update Deep Neural Networks (DNN) parameters online. It…
Dense Reward for Free in Reinforcement Learning from Human Feedback
The study focuses on Reinforcement Learning from Human Feedback (RLHF) and how it can be optimized. Traditionally,…
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
The article presents Graph-Mamba, a model developed to improve long-range context modeling in graph networks. Traditional attention…
Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction
The research by Rohan Alur, Manish Raghavan, and Devavrat Shah introduces a novel framework for incorporating human…
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
The study investigates the capabilities of Large Language Models (LLMs) in performing zero-shot tasks such as time-series…
Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents
The research paper presents a new framework called ‘Formal-LLM’ that integrates the expressiveness of natural language with…
Signal Quality Auditing for Time-series Data
The article, ‘Signal Quality Auditing for Time-series Data’, discusses the significance of signal quality assessment (SQA) in…
Distilling Conditional Diffusion Models for Offline Reinforcement Learning through Trajectory Stitching
The article discusses the use of deep generative models in offline reinforcement learning and the computational challenges…