GPT-f, a transformer-based prover for Metamath, generated new short proofs that were accepted into the main library—the first such contribution from a deep-learning system.
Deep residual learning for image recognition
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π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
OpenVLA-OFT fine-tuning boosts LIBERO success rate from 76.5% to 97.1%, speeds action generation 26x, and outperforms baselines on real bimanual dexterous tasks.
Octo is an open-source transformer-based generalist robot policy pretrained on 800k trajectories that serves as an effective initialization for finetuning across diverse robotic platforms.
LPS uses a second-order neural network to learn an end-to-end metric for second-order parameter similarity and introduces the ModelSet500 benchmark with 500 trained models.
A deep convolutional autoencoder compression framework jointly optimized with face recognition achieves higher verification accuracy on LFW images than JPEG2000 or JPEG.
Geo-LoFTR is a geometry-aided deep learning model for map-based localization that outperforms prior methods under large illumination and scale variations on simulated and real Mars imagery.
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.
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Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
OpenVLA-OFT fine-tuning boosts LIBERO success rate from 76.5% to 97.1%, speeds action generation 26x, and outperforms baselines on real bimanual dexterous tasks.