​ The challenge of matching human preferences to big pretrained models has gained prominence in the study as these models have grown in performance. This alignment becomes particularly challenging when there are unavoidably poor behaviours in bigger datasets. For this issue, reinforcement learning from human… Read More »Stanford and UT Austin Researchers Propose Contrastive Preference Learning (CPL): A Simple Reinforcement Learning RL-Free Method for RLHF that Works with Arbitrary MDPs and off-Policy Data Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ Researchers have challenged the prevailing belief in the field of computer vision that Vision Transformers (ViTs) outperform Convolutional Neural Networks (ConvNets) when given access to large web-scale datasets. They introduce a ConvNet architecture called NFNet, which is pre-trained on a massive dataset called JFT-4B,… Read More »Is ConvNet Making a Comeback? Unraveling Their Performance on Web-Scale Datasets and Matching Vision Transformers Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​ Language models like GPT-3 are designed to be neutral and generate text based on the patterns they’ve learned in the data. They don’t have inherent sentiments or emotions. If the data used for training contains biases, these biases can be reflected in the model’s… Read More »How Effective are Self-Explanations from Large Language Models like ChatGPT in Sentiment Analysis? A Deep Dive into Performance, Cost, and Interpretability Mohammad Arshad Artificial Intelligence Category – MarkTechPost

​ Despite having some parallels to other sequence modeling issues, like text, audio, or video, time series has two characteristics that make it particularly difficult. Aggregated time series datasets frequently include sequences from drastically varied sources, occasionally with missing values, in contrast to video or… Read More »Researchers from CMU and NYU Propose LLMTime: An Artificial Intelligence Method for Zero-Shot Time Series Forecasting with Large Language Models (LLMs) Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ The fields of Artificial Intelligence and Machine Learning are consistently becoming more and more prevalent. One of the major concerns in these domains has been the capacity of machines to replicate the intricacy of human cognition and language. The question still arises whether robots… Read More »Unlocking Systematic Compositionality in Neural Networks: A Breakthrough with Meta-Learning for Compositionality (MLC) Approach Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​ ULTRA is a model designed to learn universal and transferable graph representations for knowledge graphs (KGs). ULTRA creates relational illustrations by conditioning them on interactions, enabling it to generalise to any KG with different entity and relation vocabularies. A pre-trained ULTRA model exhibits impressive… Read More »Meet ULTRA: A Pre-Trained Foundation Model for Knowledge Graph Reasoning that Works on Any Graph and Outperforms Supervised SOTA Models on 50+ Graphs Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ A paradigm change in multimodal learning has occurred thanks to the contributions of large multimodal foundation models like CLIP, Flamingo, and Stable Diffusion, enabling previously unimaginable improvements in image generation and zero-shot generalization. These baseline models are generally trained on big, web-scale, static datasets.… Read More »How to Keep Foundation Models Up to Date with the Latest Data? Researchers from Apple and CMU Introduce the First Web-Scale Time-Continual (TiC) Benchmark with 12.7B Timestamped Img-Text Pairs for Continual Training of VLMs Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost