Papers
Scalable and Efficient Non-adaptive Deterministic Group Testing
Dariusz Kowalski, Dominik Pajak
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Zhiqi Bu, Jialin Mao, Shiyun Xu
Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring
Samarth Gupta, Saurabh Amin
Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees
Avinandan Bose, Arunesh Sinha, Tien Mai
Scalable Infomin Learning
Yanzhi Chen, weihao sun, Yingzhen Li et al.
Scalable Interpretability via Polynomials
Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
Jiayu Chen, Jingdi Chen, Tian Lan et al.
Scalable Neural Video Representations with Learnable Positional Features
Subin Kim, Sihyun Yu, Jaeho Lee et al.
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
Andrea Tirinzoni, Matteo Papini, Ahmed Touati et al.
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions
Andrew Jesson, Alyson Douglas, Peter Manshausen et al.
Scale-invariant Learning by Physics Inversion
Philipp Holl, Vladlen Koltun, Nils Thuerey
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization
Junru Wu, Yi Liang, feng han et al.
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
Dongze Lian, Daquan Zhou, Jiashi Feng et al.
SCAMPS: Synthetics for Camera Measurement of Physiological Signals
Daniel McDuff, Miah Wander, Xin Liu et al.
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Minhao LIU, Ailing Zeng, Muxi Chen et al.
SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
Xiyue Wang, Jinxi Xiang, Jun Zhang et al.
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
Antoine Guedon, Pascal Monasse, Vincent Lepetit
Score-Based Diffusion meets Annealed Importance Sampling
Arnaud Doucet, Will Grathwohl, Alexander G Matthews et al.
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
Dohyun Kwon, Ying Fan, Kangwook Lee
Score-Based Generative Models Detect Manifolds
Jakiw Pidstrigach
Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition
Yichao Cao, Xiu Su, Qingfei Tang et al.
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
Ruibo Liu, Chenyan Jia, Ge Zhang et al.
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
Tanguy Marchand, Boris Muzellec, Constance Béguier et al.
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Ran Liu, Mehdi Azabou, Max Dabagia et al.
Segmenting Moving Objects via an Object-Centric Layered Representation
Junyu Xie, Weidi Xie, Andrew Zisserman