Auguste Hadamard
PulseAugur coverage of Auguste Hadamard — every cluster mentioning Auguste Hadamard across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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PrismQuant framework optimizes LLM quantization for improved performance
Researchers have introduced PrismQuant, a novel framework designed to optimize grouped quantizers for large language models. This method focuses on aligning activation eigenspaces with the quantizer's subspace, leading …
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New LLM quantization methods promise reduced memory and faster inference · 2 sources tracked
Two new research papers introduce novel methods for quantizing large language models (LLMs) to reduce their memory footprint and improve inference speed. SpectrumQuant proposes a data-aware orthogonal transform method t…
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CNet framework enables complex-valued deep learning with Wirtinger derivatives
Researchers have developed CNet, a C++/CUDA framework designed for training deep complex-valued neural networks (CVNNs) and optimizing complex-valued functions using Wirtinger derivatives. This framework adopts a physic…
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CURIO framework enhances LLM open-ended discovery with curiosity-driven learning
Researchers have introduced CURIO, a novel framework for test-time learning that enhances open-ended discovery in large language models. CURIO integrates an Intrinsic Curiosity World Model (ICWM) to provide prediction-e…
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New research explores advanced quantization techniques for LLMs · 10 sources tracked
Multiple research papers introduce novel techniques for quantizing large language models (LLMs) to reduce their computational and memory footprints. These methods aim to improve efficiency without significantly sacrific…
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Uniform INT4 quantization outperforms NVFP4 on real gradient tensors
A study comparing quantization palettes for gradient tensors found that a uniform INT4 palette outperformed NVIDIA's NVFP4 palette on real-world training data. The research suggests that the random Hadamard rotation, of…
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RoPE-Aligned Rotations Fail to Improve 4-Bit Quantization Accuracy
A new research paper explores the effectiveness of RoPE-aligned Q/K rotations for dynamic 4-bit quantization in language models. The study found that while pairwise rotations can commute with RoPE, they do not improve a…
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New research predicts neural network training efficiency with random reparameterizations
A new research paper introduces Random Mapping Networks (RaMaN), a method designed to predict when random low-dimensional reparameterizations can effectively train neural networks. The paper proposes an orientation-reso…
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New technique improves Transformer KV cache compression
Researchers have developed Codec-Gauge, a post-training layer designed to improve the compression of Key-Value (KV) caches in long-context Transformer models. This method learns orthogonal channel transforms that optimi…
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New KReTTaH framework offers data-free imputation via tensor trains
A new framework called KReTTaH has been introduced for multi-way data imputation, utilizing kernel regression with tensor trains and Hadamard overparameterization. This method is designed to be training-data-free, inter…