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Found 9 papers, 5 papers with code
Accelerated Parameter-Free Stochastic Optimization
We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters.
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data.
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
Empirically, we consider a broad range of vision and language transfer learning tasks, and show that DoG's performance is close to that of SGD with tuned learning rate.
Efficient Long-Text Understanding with Short-Text Models
Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles and long documents, due to their quadratic complexity.
Ranked #6 on
Long-range modeling
on SCROLLS
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments
Neural scaling laws define a predictable relationship between a model's parameter count and its performance after training in the form of a power law.
SCROLLS: Standardized CompaRison Over Long Language Sequences
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild.
Ranked #8 on
Long-range modeling
on SCROLLS
Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics
We address this limitation by introducing Feature Vectors, a new global interpretability method designed for tabular datasets.
Achieving Model Robustness through Discrete Adversarial Training
In this work, we address this gap and leverage discrete attacks for online augmentation, where adversarial examples are generated at every training step, adapting to the changing nature of the model.
Scene Graph to Image Generation with Contextualized Object Layout Refinement
We empirically show on the COCO-STUFF dataset that our approach improves the quality of both the intermediate layout and the final image.
