Education
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
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.
Best-of-All-WorldsBoundsfor OnlineLearningwithFeedbackGraphs
Here, θ() is the clique coveringnumberofthegraph. Online learning models a repeated interaction between a learner and an environment.
class distribution is shared across datasets. (b) Compared to supervised learning (SL), CLIP's performance (measured by accuracy) is less biased by data frequency, and the classifier is notably
Severe data imbalance naturally exists among web-scale vision-language datasets.
No-regret Online Learning over Riemannian Manifolds
We establish upper bounds on the regrets of the problem with respect to time horizon, manifold curvature, and manifold dimension.