Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's parameters or the cl...
Overview Introducing Sentira CORE, an interactive NLP & ML‑powered tool that analyzes text emotions with high precision. It detects six emotions — Joy, Love, S...
Benchmarks Measure Models, Not Systems python model.fitX, y Timing starts before .fit and ends after. What’s missing? - Data loading - Data cleaning - Feature...
LLM-powered Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex domains but suffer from inherent fragility and opaque failure mechani...
Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input graphs are distributed,...
Federated Learning (FL) enables a group of clients to collaboratively train a model without sharing individual data, but its performance drops when client data ...
Numerous lines of aim to control model disagreement -- the extent to which two machine learning models disagree in their predictions. We adopt a simple and stan...
We identify occlusion reasoning as a fundamental yet overlooked aspect for 3D layout-conditioned generation. It is essential for synthesizing partially occluded...
A dataset server must often distribute the same large payload to many clients, incurring massive communication costs. Since clients frequently operate on divers...
The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Rece...
Recent work such as AlphaEvolve has shown that combining LLM-driven optimization with evolutionary search can effectively improve programs, prompts, and algorit...
Standard mixed-precision training of neural networks requires many bytes of accelerator memory for each model parameter. These bytes reflect not just the parame...
