Ruby Finds Unsafe Rust Regions in Stripped Binaries
Instruction-level binary features train a classifier that recovers 91.75% of unsafe regions at a 6.16% false positive rate.
All aspects of machine learning research including supervised, unsupervised, and reinforcement learning.
Instruction-level binary features train a classifier that recovers 91.75% of unsafe regions at a 6.16% false positive rate.
Module-specific NVFP4 quantization covers projections, optimizers, and attention, reaching a 1.47% loss gap on 3B/64B-token pretraining.
The Transformer replaces recurrence and convolution with multi-head self-attention, reaching 28.4 BLEU on WMT 2014 English-German with shorter training.
Platform-conditioned teacher selection distills desktop and mobile policies into one continual learner, reaching 38.2% OSWorld and 12.0% MobileWorld success.
Generating natural language explanations then verifying them symbolically outperforms chain-of-thought prompting.
Explicit multi-view geometry constraints improve monocular depth without any ground-truth supervision.
Random subspace methods achieve convergence rates dependent on intrinsic dimensionality, not ambient dimension.
A framework bridges classical optimization and deep learning with provable guarantees for learned optimizers.
A hierarchical sparse decomposition achieves sub-quadratic long-range attention with minimal quality loss.
AI-generated feedback guided by explicit principles cuts the need for human reviewers in safety fine-tuning.