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Traditional machine learning on top of Nx (⭐ 490)
Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learn...
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Archived 13 December 2022 at the Wayback Machine. National Park Service. November 6, 2021. Retrieved February 11, 2024. Donlan RM (2002). "Biofilms: Microbial
Feb 25, 2019 · Those are probably the best machine translators on the market. Deepl has the edge over Google Translate, but neither of them are 100% reliable. Both AFAIK use neural net technology, so …
Points: 1 | Comments: 0 | Author: PaulHoule
The accurate modeling of real-world systems and physical interactions is a common challenge towards the resolution of robotics tasks. Machine learning approaches have demonstrated significant results in the modeling of complex systems (e.g., articula...
As inference on Large Language Models (LLMs) emerges as an important workload in machine learning applications, weight quantization has become a standard technique for efficient GPU deployment. Quantization not only reduces model size, but has also b...
Recently, a new training oaxe loss has proven effective to ameliorate the effect of multimodality for non-autoregressive translation (NAT), which removes the penalty of word order errors in the standard cross-entropy loss. Starting from the intuition...
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Hello, everyone. As promised, I'll write a longer post about how I entered the world of ML, hoping it will help someone shape their path. I'll include links to all the useful materials I used alongsid...
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In physics and engineering, efficiency is the ratio between the amount of energy a machine needs to make it work, and the amount it produces.
Learning node representations is a fundamental problem in graph machine learning. While existing embedding methods effectively preserve local similarity measures, they often fail to capture global functions like graph distances. Inspired by Bourgain'...
Federated Learning (FL) offers a promising solution to the privacy concerns associated with centralized Machine Learning (ML) by enabling decentralized, collaborative learning. However, FL is vulnerable to various security threats, including poisonin...
As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become more vulnerable to label poisoning attacks. These attacks, in which a...
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