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Random Projections with Asymmetric Quantization
Neural Information Processing Systems http://nips.cc/
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
ReFIR: GroundingLargeRestorationModels withRetrievalAugmentation
Recently, diffusion models [10, 11] have emerged as a promising alternative, delivering noteworthy results in real-world image restoration [12, 13, 14].
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Chaitanya Ryali, Gautam Reddy, Angela J. Yu
Neural Information Processing Systems http://nips.cc/
Space and Time Efficient Kernel Density Estimation in High Dimensions
Arturs Backurs, Piotr Indyk, Tal Wagner
However, their data structure requires a significantly increased super-linear storage space, as well as super-linear preprocessing time. These limitations inhibit the practical applicability of their approach on large datasets.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song
Neural Information Processing Systems http://nips.cc/
Evolved Policy Gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, Pieter Abbeel
The idea is to evolve a differentiable loss function, such thatanagent, which optimizes itspolicytominimize thisloss, willachieve highrewards.
