Quickest Change Detection for Unnormalized Statistical Models.
IEEE Trans. Inf. Theory, February, 2024
A Data-Efficient Deep Learning Method for Rough Surface Clutter Reduction in GPR Images.
IEEE Trans. Geosci. Remote. Sens., 2024
MAP: Multi-Human-Value Alignment Palette.
CoRR, 2024
DynamicFL: Federated Learning with Dynamic Communication Resource Allocation.
CoRR, 2024
ColA: Collaborative Adaptation with Gradient Learning.
CoRR, 2024
Large Deviation Analysis of Score-Based Hypothesis Testing.
IEEE Access, 2024
ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
Efficient and Collaborative Methods for Distributed Machine Learning.
PhD thesis, 2023
Robust Quickest Change Detection for Unnormalized Models.
Proceedings of the Uncertainty in Artificial Intelligence, 2023
Semi-Supervised Federated Learning for Keyword Spotting.
Proceedings of the IEEE International Conference on Multimedia and Expo Workshops, 2023
Pruning Deep Neural Networks from a Sparsity Perspective.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Score-based Quickest Change Detection for Unnormalized Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation.
CoRR, 2022
Score-Based Hypothesis Testing for Unnormalized Models.
IEEE Access, 2022
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow.
Proceedings of the Data Compression Conference, 2022
Multimodal Controller for Generative Models.
Proceedings of the Computer Vision and Machine Intelligence, 2022
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders.
Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2022, Pacific Grove, CA, USA, October 31, 2022
On Statistical Efficiency in Learning.
IEEE Trans. Inf. Theory, 2021
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models.
CoRR, 2021
Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders.
CoRR, 2021
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients.
CoRR, 2021
Gradient Assisted Learning.
CoRR, 2021
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.
Proceedings of the 9th International Conference on Learning Representations, 2021
Speech Emotion Recognition with Dual-Sequence LSTM Architecture.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
Deep Clustering of Compressed Variational Embeddings.
Proceedings of the Data Compression Conference, 2020
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression.
Proceedings of the Data Compression Conference, 2020
Distributed Lossy Image Compression with Recurrent Networks.
CoRR, 2019
Restricted Recurrent Neural Networks.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019
A Penalized Method for the Predictive Limit of Learning.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018