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StepbaQ: Stepping backward as Correction for Quantized Diffusion Models
Quantization of diffusion models has attracted considerable attention due to its potential to enable various applications on resource-constrained mobile devices.
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
However, such pessimism for out-of-sample data could be too restricted and sample inefficient, as not all out-of-sample(unseen) states are not generalizable [20].
Episodic Memory in Lifelong Language Learning
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
Neural Information Processing Systems http://nips.cc/
Fast and Effective Robustness Certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev
Neural Information Processing Systems http://nips.cc/
Multi-relationalPoincaréGraphEmbeddings
Hyperbolic embeddings haverecently gained attention in machine learning due totheir ability torepresent hierarchical data more accurately and succinctly than their Euclidean analogues.
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines
In this paper, we introduce alowrank interaction and sparse additive effects(LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects andinteractions simultaneously.
Multiclass Learning from Contradictions
Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
Neural Information Processing Systems http://nips.cc/
Proximal Graphical Event Models
Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
Neural Information Processing Systems http://nips.cc/