Noise-aware training for analog hardware: accuracy collapses at a threshold rath

<!-- SC_OFF --><div class="md"><p>Hi, <a href="/r/MachineLearning">r/MachineLearning</a>: has much research been done in large-scale training scenarios where undesirable data has

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1s7p0au/tracer_learntodefer_for_llm_classification_with/"> <img alt="TRACER: Learn-to-Defer for LLM

<!-- SC_OFF --><div class="md"><p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2604.21395v2"><strong>https://arxiv.org/abs/2604.21395v2</strong></a></p> <p>For years,

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1s0y008/r_designing_ai_chip_software_and_hardware/"> <img alt="[R] Designing AI Chip Software and

<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone

<p>I'm a 10th grade student teaching myself Rust by building something instead of just reading theory. The project is called Green-AI — an attempt to explore more energy-efficient

<!-- SC_OFF --><div class="md"><p>Hi all, so im making a binary noise classification model. I have training data with timestamps of noise and some with verified some with

<!-- SC_OFF --><div class="md"><p>Analog IC layout is a notoriously hard AI benchmark: spatial reasoning, multi-objective optimization (matching, parasitics, routing), and no