Tag: deep-learning
All the articles with the tag "deep-learning".
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The Credit Assignment Problem: From Rosenblatt's Perceptron to Backpropagation to Quantum Gradients
A long-form, citation-grounded history of the credit assignment problem — the core question of how to apportion blame for a global error across the internal parameters of a learning machine. From Rosenblatt's 1958 perceptron rule, through Linnainmaa, Werbos, and the 1986 Rumelhart-Hinton-Williams paper, to modern alternatives (feedback alignment, equilibrium propagation, predictive coding, forward-forward, synthetic gradients), reinforcement-learning credit assignment, and the frontier of quantum gradients (parameter-shift rule, quantum natural gradient, HHL, and the Abbas et al. NeurIPS 2023 impossibility result).
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Notable Papers from ICLR, ICML, NeurIPS, CVPR, EMNLP (2025–2026): An Award-Grounded Reading List
A short, hyperlinked reading list of award-winning and outstanding papers from ICLR 2025/2026, ICML 2025, NeurIPS 2025, CVPR 2025, and EMNLP 2025. One- to three-line summaries with direct arXiv and venue links.
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The Edge of Artificial Intelligence Research: A Citation-Grounded Survey from ICML, ICLR, NeurIPS (2023–2026)
A domain-by-domain survey of the artificial intelligence research frontier, grounded in specific papers from ICML, ICLR, NeurIPS, and adjacent venues (CoRL, CVPR, Nature). Covers natural language processing, speech, video, sound, robotics/VLA, biology, 3D generation, diffusion architecture, and the renaissance of reinforcement learning and world models. The through-line: pretraining is no longer the frontier — test-time compute, generative simulators, and embodied grounding are.