Papers
Calibration and Consistency of Adversarial Surrogate Losses
Pranjal Awasthi, Natalie Frank, Anqi Mao et al.
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Sakshi Varshney, Vinay Kumar Verma, P. K. Srijith et al.
Can contrastive learning avoid shortcut solutions?
Joshua W. Robinson, Li Sun, Ke Yu et al.
Can fMRI reveal the representation of syntactic structure in the brain?
Aniketh Janardhan Reddy, Leila Wehbe
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta et al.
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression
Zhize Li, Peter Richtarik
Can multi-label classification networks know what they don’t know?
Haoran Wang, Weitang Liu, Alex Bocchieri et al.
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Weiwei Sun, Andrea Tagliasacchi, Boyang Deng et al.
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
Will Stephenson, Zachary Frangella, Madeleine Udell et al.
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
Sandesh Kamath, Amit Deshpande, Subrahmanyam Kambhampati Venkata et al.
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta et al.
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
Michael Boratko, Dongxu Zhang, Nicholas Monath et al.
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Tatiana Likhomanenko, Qiantong Xu, Gabriel Synnaeve et al.
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
Joy Hsu, Jeffrey Gu, Gong Wu et al.
Cardinality constrained submodular maximization for random streams
Paul Liu, Aviad Rubinstein, Jan Vondrak et al.
Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Ide, Georgios Kollias, Dzung Phan et al.
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator
Alek Dimitriev, Mingyuan Zhou
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej et al.
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
Gal Greshler, Tamar Shaham, Tomer Michaeli
CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon et al.
Causal Abstractions of Neural Networks
Atticus Geiger, Hanson Lu, Thomas Icard et al.
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort et al.
Causal Bandits with Unknown Graph Structure
Yangyi Lu, Amirhossein Meisami, Ambuj Tewari
Causal Effect Inference for Structured Treatments
Jean Kaddour, Yuchen Zhu, Qi Liu et al.