Papers
LLMs Can Reason Faster Only If We Let Them
Bilgehan Sel, Lifu Huang, Naren Ramakrishnan et al.
Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching
Nabeel Seedat, Mihaela Van Der Schaar
Differentially Private Federated k-Means Clustering with Server-Side Data
Jonathan Scott, Christoph H. Lampert, David Saulpic
Cover learning for large-scale topology representation
Luis Scoccola, Uzu Lim, Heather A. Harrington
Learning Representations of Instruments for Partial Identification of Treatment Effects
Jonas Schweisthal, Dennis Frauen, Maresa Schröder et al.
The Disparate Benefits of Deep Ensembles
Kajetan Schweighofer, Adrian Arnaiz-Rodriguez, Sepp Hochreiter et al.
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
Luca M. Schulze Buschoff, Konstantinos Voudouris, Elif Akata et al.
Mastering Board Games by External and Internal Planning with Language Models
John Schultz, Jakub Adamek, Matej Jusup et al.
Adjustment for Confounding using Pre-Trained Representations
Rickmer Schulte, David Rügamer, Thomas Nagler
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Jan Schuchardt, Mina Dalirrooyfard, Jed Guzelkabaagac et al.
Temperature-Annealed Boltzmann Generators
Henrik Schopmans, Pascal Friederich
Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
Mark Schöne, Babak Rahmani, Heiner Kremer et al.
Hyperband-based Bayesian Optimization for Black-box Prompt Selection
Lennart Schneider, Martin Wistuba, Aaron Klein et al.
Generative Intervention Models for Causal Perturbation Modeling
Nora Schneider, Lars Lorch, Niki Kilbertus et al.
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
Thomas Schmied, Thomas Adler, Vihang Prakash Patil et al.
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
Laines Schmalwasser, Niklas Penzel, Joachim Denzler et al.
Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
Connor Schenck, Isaac Reid, Mithun George Jacob et al.
The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
Fabian Schaipp, Alexander Hägele, Adrien Taylor et al.
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
Rylan Schaeffer, Hailey Schoelkopf, Brando Miranda et al.
How Do Large Language Monkeys Get Their Power (Laws)?
Rylan Schaeffer, Joshua Kazdan, John Hughes et al.
Symmetry-Robust 3D Orientation Estimation
Christopher Scarvelis, David Benhaim, Paul Zhang
ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
Utkarsh Saxena, Sayeh Sharify, Kaushik Roy et al.
Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic
Eshika Saxena, Alberto Alfarano, Emily Wenger et al.
Natural Perturbations for Black-box Training of Neural Networks by Zeroth-Order Optimization
Hiroshi Sawada, Kazuo Aoyama, Yuya Hikima
NestQuant: nested lattice quantization for matrix products and LLMs
Semyon Savkin, Eitan Porat, Or Ordentlich et al.