Machine Learning

Incorporating demonstrating examples, known as in-context learning (ICL), significantly enhances large language models (LLMs) and large multimodal models (LMMs) without requiring parameter updates. Recent studies confirm the efficacy of few-shot multimodal ICL, particularly in improving LMM...
Machine learning models, which can contain billions of parameters, require sophisticated methods to fine-tune their performance efficiently. Researchers aim to enhance the accuracy of these models while minimizing the computational resources needed. This improvement is crucial...

Machine Learning Revolutionizes Path Loss Modeling with Simplified Features

Accurate propagation modeling is paramount for effective radio deployments, coverage analysis, and interference mitigation in wireless communications. Path loss modeling, a widely adopted approach,...

This AI Paper Introduces Rational Transfer Function: Advancing Sequence Modeling with FFT Techniques

State-space models (SSMs) are crucial in deep learning for sequence modeling. They represent systems where the output depends on both current and past inputs....

Enhancing Graph Classification with Edge-Node Attention-based Differentiable Pooling and Multi-Distance Graph Neural Networks GNNs

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global...

Model Explorer: A Powerful Graph Visualization Tool that Helps One Understand, Debug, and Optimize Machine Learning Models

Machine Learning (ML) is everywhere these days, playing a crucial role in countless fields worldwide. Its applications are endless, and we rely on it...

The Pursuit of the Platonic Representation: AI’s Quest for a Unified Model of Reality

As Artificial Intelligence (AI) systems advance, a fascinating trend has emerged: their representations of data across different architectures, training objectives, and even modalities seem...

This AI Research from Google DeepMind Explores the Performance Gap between Online and Offline Methods for AI Alignment

RLHF is the standard approach for aligning LLMs. However, recent advances in offline alignment methods, such as direct preference optimization (DPO) and its variants,...

Harmonics of Learning: A Mathematical Theory for the Rise of Fourier Features in Learning Systems Like Neural Networks

Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on...

CMU Researchers Propose MOMENT: A Family of Open-Source Machine Learning Foundation Models for General-Purpose Time Series Analysis

Pre-training large models on time series data faces several challenges: the lack of a comprehensive public time series repository, the complexity of diverse time...

DataSP: A Differentiable All-to-All Shortest Path Machine Learning Algorithm to Facilitate Learning Latent Costs from Trajectories

In traffic management and urban planning, the ability to learn optimal routes from demonstrations conditioned on contextual features holds significant promise. As underscored by...

Microsoft Researchers Introduce Syntheseus: A Machine Learning Benchmarking Python Library for End-to-End Retrosynthetic Planning

A resurgence of interest in the computer automation of molecular design has occurred throughout the last five years, thanks to advancements in machine learning,...

Vidur: A Large-Scale Simulation Framework Revolutionizing LLM Deployment Through Cost Cuts and Increased Efficiency

Large language models (LLMs) such as GPT-4 and Llama are at the forefront of natural language processing, enabling various applications from automated chatbots to...

MISATO: A Machine Learning Dataset of Protein-Ligand Complexes for Structure-based Drug Discovery

In the dynamic field of AI technology, a pressing challenge for the drug discovery (DD) community, especially in structural biology and computational chemistry, is...

Snowflake AI Research Team Unveils Arctic: An Open-Source Enterprise-Grade Large Language Model (LLM) with...

0
Snowflake AI Research has launched the Arctic, a cutting-edge open-source large language model (LLM) specifically designed for enterprise AI applications, setting a new standard...

Google DeepMind Releases RecurrentGemma: One of the Strongest 2B-Parameter Open Language Models Designed for...

0
Language models are the backbone of modern artificial intelligence systems, enabling machines to understand and generate human-like text. These models, which process and predict...

Finally, the Wait is Over: Meta Unveils Llama 3, Pioneering a New Era in...

0
Meta has revealed its latest large language model, the Meta Llama 3, which is a major breakthrough in the field of AI. This new model is not just...

TrueFoundry Releases Cognita: An Open-Source RAG Framework for Building Modular and Production-Ready Applications

0
The field of artificial intelligence is rapidly evolving, andย takingย a prototype to production stage can be quite challenging. However, TrueFoundry has recently introduced a new...

Meet Zamba-7B: Zyphra’s Novel AI Model That’s Small in Size and Big on Performance

0
In the race to create more efficient and powerful AI models, Zyphra has unveiled a significant breakthrough with its new Zamba-7B model. This compact,...

Recent articles

๐Ÿ ๐Ÿ Join the Fastest Growing AI Research Newsletter Read by Researchers from Google + NVIDIA + Meta + Stanford + MIT + Microsoft and many others...

X