Machine Learning

Can We Drastically Reduce AI Training Costs? This AI Paper from MIT, Princeton, and Together AI Unveils How BitDelta Achieves Groundbreaking Efficiency in Machine...

Training Large Language Models (LLMs) involves two main phases: pre-training on extensive datasets and fine-tuning for specific tasks. While pre-training requires significant computational resources,...

Meet VLM-CaR (Code as Reward): A New Machine Learning Framework Empowering Reinforcement Learning with Vision-Language Models

Researchers from Google DeepMind have collaborated with Mila, and McGill University defined appropriate reward functions to address the challenge of efficiently training reinforcement learning...

Researchers from AWS AI Labs and USC Propose DeAL: A Machine Learning Framework that Allows the User to Customize Reward Functions and Enables Decoding-Time...

A crucial challenge at the core of the advancements in large language models (LLMs) is ensuring that their outputs align with human ethical standards...

Researchers from NVIDIA and the University of Maryland Propose ODIN: A Reward Disentangling Technique that Mitigates Hacking in Reinforcement Learning from Human Feedback (RLHF)

The well-known Artificial Intelligence (AI)-based chatbot, i.e., ChatGPT, which has been built on top of GPT's transformer architecture, uses the technique of Reinforcement Learning...

Can Machine Learning Models Be Fine-Tuned More Efficiently? This AI Paper from Cohere for AI Reveals How REINFORCE Beats PPO in Reinforcement Learning from...

The alignment of Large Language Models (LLMs) with human preferences has become a crucial area of research. As these models gain complexity and capability,...

Can Machine Learning Teach Robots to Understand Us Better? This Microsoft Research Introduces Language Feedback Models for Advanced Imitation Learning

The challenges in developing instruction-following agents in grounded environments include sample efficiency and generalizability. These agents must learn effectively from a few demonstrations while...

This Machine Learning Research Introduces Premier-TACO: A Robust and Highly Generalizable Representation Pretraining Framework for Few-Shot Policy Learning

In our ever-evolving world, the significance of sequential decision-making (SDM) in machine learning cannot be overstated. Unlike static tasks, SDM reflects the fluidity of...

Revolutionizing 3D Scene Reconstruction and View Synthesis with PC-NeRF: Bridging the Gap in Sparse LiDAR Data Utilization

The relentless quest for autonomous vehicles has pivoted around the ability to interpret and navigate complex environments with precision and reliability. Central to this...

Shattering AI Illusions: Google DeepMind’s Research Exposes Critical Reasoning Shortfalls in LLMs!

LLMs, which have been lauded for their exceptional performance across a spectrum of reasoning tasks, from STEM problem-solving to code generation, often surpassing human...

Meet Optuna: An Automatic Hyperparameter Optimization Software Framework Designed for Machine Learning

In machine learning, finding the perfect settings for a model to work at its best can be like looking for a needle in a...

Researchers from CMU and Peking Introduces ‘DiffTOP’ that Uses Differentiable Trajectory Optimization to Generate the Policy Actions for Deep Reinforcement Learning and Imitation Learning

According to recent studies, a policy's depiction can significantly affect learning performance. Policy representations such as feed-forward neural networks, energy-based models, and diffusion have...

This Machine Learning Research from Yale and Google AI Introduce SubGen: An Efficient Key-Value Cache Compression Algorithm via Stream Clustering

Large language models (LLMs) face challenges in generating long-context tokens due to high memory requirements for storing all previous tokens in the attention module....

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