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Small batch deep reinforcement learning
Contrastingly, others have observed that larger batch sizes tend to converge to "sharper" optimization
Small batch deep reinforcement learning
Since the policy used to collect transitions is changing throughout learning, the replay memory contains data coming from a mixture of policies (that differ from the agent's current policy), and
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
The techniques of machine unlearning, also known as concept erasing, have been developed to address these risks.
Precision-Recall Balanced Topic Modelling
Neural Information Processing Systems http://nips.cc/
On the Computational Complexity of Private High-dimensional Model Selection
In this paper, we consider the problem of private model selection in high-dimensional sparse regression which has been one of the central topics in statistical research over the past decade.
Few-ShotContinualActiveLearningbyaRobot
The framework also uses uncertainty measures on the Gaussian representations of thepreviously learned classes tofindthemost informativesamples tobelabeled in an increment. We evaluate our approach on the CORe-50 dataset and on a real humanoid robot for the object classification task.
A coupled autoencoder approach for multi-modal analysis of cell types
Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexityofbrain circuits canbereduced, andeffectivelystudied bymeans of interactions betweencelltypes.