This page shows the most relevant public items for Machine Learning, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Jun 12, 2017 • arXiv • Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder ...
Paper • Feb 13, 2026 • arXiv • Zijing Ou, Jacob Si, Junyi Zhu, Yingzhen Li
Diffusion alignment aims to optimize diffusion models for downstream objectives. While existing methods based on RL achieve success, they often suffer from reward over-optimization and mode collaps...
Blog • Feb 17, 2026 • Towardsai • Florian June
Traditional Retrieval-Augmented Generation (RAG) is becoming a commodity; the next frontier is 'Cog-RAG.' This post details a new architecture where an agentic 'brain' evaluates a query, identifies...
Paper • Mar 4, 2026 • arXiv • Yong Liu, Xingjian Su, Shiyu Wang, Haoran Zhang
While text and image foundation models have scaled massively, time-series forecasting has lagged behind due to architectural constraints. Timer-S1 introduces a billion-parameter foundation model sp...
Paper • Feb 28, 2026 • arXiv • Runquan Gui, Yafu Li, Xiaoye Qu, Ziyan Liu, Yeqiu Cheng, Yu Cheng
Large Language Models frequently produce correct final answers based on flawed or unfaithful intermediate reasoning steps. This paper proposes Step-Level Faithfulness Maximization, a training parad...
Book • Aug 24, 2021 • Amazon • Valliappa Lakshmanan, Martin Görner, Ryan Gillard
Employing machine learning models to extract information from images can be daunting for software developers. This book provides intuitive explanations of visual architectures alongside practical c...
Blog • Jan 15, 2026 • Medium • Elena Rossi
The release of advanced reasoning models has completely shifted how developers build autonomous systems in early 2026. This post details how open weights models are replacing expensive proprietary ...
Paper • Dec 14, 2023 • arXiv • Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeff Wu
As AI models become increasingly capable, we will eventually face the challenge of superalignment: how can humans supervise AI systems that are much smarter than them? To study this empirically tod...
Paper • Mar 29, 2022 • arXiv • Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Laurent Sifre
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly under...
Paper • Feb 20, 2026 • bioRxiv • L. Zheng, J. Drucker, A. Chen, W. Wang
Designing therapeutic peptides requires balancing binding affinity with complex ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, a task that overwhelms traditional ...
Paper • Feb 10, 2026 • bioRxiv • Eduardo da Veiga Beltrame
This paper presents a systematic benchmark of architectural hyperparameters for variational autoencoder (VAE) methods in single-cell RNA-seq batch integration. The study compares scVI, MrVI, and LD...
Paper • Feb 16, 2026 • bioRxiv • Anonymous Team
The paper introduces an automated deep learning tool for the real-time assessment of pain in mice using the Mouse Grimace Scale (MGS). The model was trained on a large and diverse dataset with non-...
Paper • Feb 23, 2026 • bioRxiv • Zhang, X., Jeong, E., Yan, C., Feng, Y., Lyu, L., Guo, X., Chen, Y.
CellAwareGNN is a new graph neural network (GNN) foundation model that integrates single-cell enhanced knowledge graphs for drug indication prediction. By modeling the complex relationships between...
Paper • Feb 1, 2026 • arXiv • Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin
We present algorithms for diffusion model sampling which obtain δ-error in polylog(1/δ) steps, given access to eO(δ)-accurate score estimates in L2. This is an exponential improvement over all prev...
Paper • Mar 4, 2026 • arXiv • Jiawei Chen, Tianzhuo Yang, Guoxi Zhang, Jiaming Ji, Yaodong Yang, Juntao Dai
Aligning large language models to individual user values without compromising the core safety parameters of the foundation model is notoriously difficult. This paper introduces VISA, a shielded ada...
Paper • Feb 17, 2026 • arXiv • Moritz Miller, Florent Draye, Bernhard Schölkopf
This paper addresses the fundamental challenge of 'feature disentanglement' in modern deep learning. We propose an Orthogonality Regularization technique to identify features that are both interpre...
Blog • Feb 14, 2026 • Medium • Shane Collins
In 2026, ML has moved from the cloud to our pockets, handling the 'normal messiness' of real life. This post explores the small, invisible ways AI has cleaned up our lives, from transcripts that ha...
Paper • Dec 10, 2014 • arxiv.org • Alex Graves, Greg Wayne, Ivo Danihelka
We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Mach...
Paper • Oct 16, 2013 • arxiv.org • Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean
The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic ...
Paper • Sep 7, 2013 • arxiv.org • Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity ta...
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