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- 2,227
- Mobility network models of COVID-19 explain inequities and inform reopening1,561
A metapopulation susceptible–exposed–infectious–removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas is introduced and correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups.
- 1,322
- Distributionally Robust Neural Networks807
The results suggest that regularization is critical for worst-group performance in the overparameterized regime, even if it is not needed for average performance, and introduce and provide convergence guarantees for a stochastic optimizer for this group DRO setting.
- DataComp: In search of the next generation of multimodal datasets782
The introduction of DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl, enables training a CLIP ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, and makes the benchmark accessible to researchers with varying resources.
- OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models663
OpenFlamingo is an ongoing effort to produce an open-source replication of DeepMind's Flamingo models, and the models, training data, hyperparameters, and evaluation suite are introduced.
- Mapping the Pairwise Choices Leading from Pluripotency to Human Bone, Heart, and Other Mesoderm Cell Types490
The extrinsic signals controlling each binary lineage decision were defined, enabling us to logically block differentiation towards unwanted fates and rapidly steer pluripotent stem cells towards 80–99% pure human mesodermal lineages at most branchpoints.
- On Random Weights and Unsupervised Feature Learning467
The answer is that certain convolutional pooling architectures can be inherently frequency selective and translation invariant, even with random weights, and the viability of extremely fast architecture search is demonstrated by using random weights to evaluate candidate architectures, thereby sidestepping the time-consuming learning process.
- Are aligned neural networks adversarially aligned?415
It is shown that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, the authors can find adversarial inputs with brute force.
- 372
- Tiled convolutional neural networks353
This paper proposes tiled convolution neural networks (Tiled CNNs), which use a regular "tiled" pattern of tied weights that does not require that adjacent hidden units share identical weights, but instead requires only that hidden units k steps away from each other to have tied weights.
- OLMoE: Open Mixture-of-Experts Language Models352
This work introduces OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE), and presents various experiments on MoE training, analyzes routing in the model showing high specialization, and open-source all aspects of the work: model weights, training data, code, and logs.
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- 296
- Stronger data poisoning attacks break data sanitization defenses292
Three attacks are developed that can bypass a broad range of common data sanitization defenses, including anomaly detectors based on nearest neighbors, training loss, and singular-value decomposition, and the Karush–Kuhn–Tucker conditions.
- 2 OLMo 2 Furious286
This work describes the modified model architecture and training recipe, focusing on techniques for achieving better training stability and improved per-token efficiency, and introduces a new, specialized data mix called Dolmino Mix 1124, which significantly improves model capabilities across many downstream task benchmarks when introduced via late-stage curriculum training.
- Sparse Filtering278
This work presents sparse filtering, a simple new algorithm which is efficient and only has one hyperparameter, the number of features to learn, and evaluates it on natural images, object classification, and phone classification, showing that the method works well on a range of different modalities.
- 269
- 235
- MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning218
This paper proposes to change the static paradigm to an interactive one, develop systems that proactively ask questions to gather more information and respond reliably, and introduces an interactive MediQ benchmark and a novel question-asking system to evaluate question-asking ability in LLMs.
- Learning Deep Energy Models204
This work proposes deep energy models, which use deep feedforward neural networks to model the energy landscapes that define probabilistic models, and is able to efficiently train all layers of this model simultaneously, allowing the lower layers of the model to adapt to the training of the higher layers, and thereby producing better generative models.
- Impossibility theorems for feature attribution167
This work shows that complete and linear methods are provably less reliable than simpler methods at answering basic feature attribution questions, and provides impossibility results that highlight their failure cases.
- 144
- Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time143
Wild-Time is curate, a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification that systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning.
- The Generative AI Paradox: "What It Can Create, It May Not Understand"136
The hypothesis that models' generative capability may not be contingent upon understanding capability, and call for caution in interpreting artificial intelligence by analogy to human intelligence, is supported.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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