From the 1 of 1.8k papers with an AI index.
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- Centre National de la Recherche ScientifiqueFR421 papers
- Princeton UniversityUS420 papers
- Fermi National Accelerator LaboratoryUS416 papers
- RWTH Aachen UniversityDE400 papers
- Charles UniversityCZ395 papers
- Northwestern UniversityUS395 papers
- University of Nebraska–LincolnUS393 papers
- Tata Institute of Fundamental ResearchIN392 papers
- University of California, RiversideUS389 papers
- Florida State UniversityUS387 papers
- Rice UniversityUS387 papers
- University of Illinois ChicagoUS387 papers
10 papers · 2 filters
Open-World Class Discovery with Kernel Networks
Zifeng Wang, Batool Salehi, Andrey Gritsenko +3
We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two…
Learn-Prune-Share for Lifelong Learning
Zifeng Wang, Tong Jian, Kaushik Chowdhury +3
In lifelong learning, we wish to maintain and update a model (e.g., a neural network classifier) in the presence of new classification tasks that arrive sequentially. In this paper…
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Pengchao Han, Jihong Park, Shiqiang Wang +1
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer. However, KD requires a large volume of original data or their representatio…
Sequential Segment-based Level Generation and Blending using Variational Autoencoders
Anurag Sarkar, Seth Cooper
Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segmen…
Towards Differentially Private Text Representations
Lingjuan Lyu, Yitong Li, Xuanli He +1
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server wh…
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
Pu Zhao, Pin-Yu Chen, Payel Das +2
Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we…