arXiv AI
Jun 3
q0: Primitives for Hyper-Epoch Pretraining
arXiv:2606. 03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text.
Nowcasting Future Performance for Population Pruning in Survival-of-the-Fittest Training
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arXiv Machine Learning
Aug 11
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
arXiv:2608. 09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken.
Hugging Face Trending Papers
Aug 4
Predicting Deep Neural Network Training Outcomes from Early Training Telemetry
Large hyperparameter sweeps for deep neural networks spend substantial compute on configurations that are effectively doomed from the first few epochs. We study whether a single training run's own early telemetry - per-epoch loss, training accuracy, gradient signal-to-noise ratio, weight-norm growth, and an activation-saturation snapshot - together with its sampled hyperparameters, can predict that run's eventual outcome without reference to other runs.
arXiv AI
Jul 23
Test Case Prioritization for DNNs via Neural Collapse Instability
arXiv:2607. 20046v1 Announce Type: cross Abstract: With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important.
arXiv AI
1d ago
Big Models, Small Gains: Frozen Forecasters as Classifiers on MONSTER
arXiv:2611.06988v1 Announce Type: cross Abstract: Forecasting foundation models are increasingly used as frozen feature extractors for time series classification, but it remains unclear whether their...
arXiv Computer Vision
Sep 10
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
arXiv:2609.10311v1 Announce Type: cross Abstract: The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initial...
arXiv AI
Oct 21
The Shape of the DPO Hyperparameter Landscape: What to Measure, How It Moves with Scale, and When Not to Transfer It
arXiv:2610.27895v1 Announce Type: cross Abstract: Direct preference optimisation (DPO) is widely used for language model post-training, but its performance depends strongly on the choice of its hyper...
arXiv AI
Sep 30
Trajectory Soup: Pushing the Compute-Scaling Frontier of LLM Mid-training via Diverse Trajectories
arXiv:2609.37169v1 Announce Type: cross Abstract: Mid-training equips pretrained large language models with specialized and reasoning capabilities, but the returns of this stage are bounded since add...
arXiv AI
Oct 7
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of mainta...
arXiv AI
Sep 7
Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference
The paper demonstrates that layer dropout, also known as stochastic depth, can be effectively used in state‑of‑the‑art large language model (LLM) training. By optimizing the layer distribution, schedule, and optimizer settings, the authors show that layer dropout can reduce training loss while saving up to 25 % of training FLOPs. Additionally, layer dropout enables post‑training optimizations such as early exit and self‑speculative decoding, achieving up to 1.5× inference speedup with negligible accuracy loss across models ranging from 271 M to 8.2 B parameters and datasets up to 160 B tokens.
arXiv Machine Learning
Jun 11
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
arXiv:2606. 12278v1 Announce Type: cross Abstract: Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance.
arXiv Machine Learning
2d ago
LACS: Distribution-Sensitive Label-Agnostic Confidence Selection for Dynamic Data Pruning
arXiv:2611.05616v1 Announce Type: new Abstract: Training on large datasets motivates selecting which examples receive expensive training computation. Fixed quotas determine the amount of pruning in a...