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DMAP:a Distributed Morphological Attention Policy for Learningto Locomotewitha Changing Body
Basedontheseprinciples, weproposethe Distributed Morphological Attention Policy (DMAP) architecture (Figure 1). Weproposea Distributed Morphological Policy (DMAP) toaddressthisproblem (Figure 1).
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Taesik Gong
Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost.
IsOut-of-DistributionDetectionLearnable?
First, we find a necessary condition for the learnability of OOD detection.
Unsupervised Attention-guided Image-to-Image Translation
Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim
Neural Information Processing Systems http://nips.cc/
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
SVR methods use control variates to reduce the variance of the traditional stochastic gradient descent (SGD) estimate f0i(w) of the full gradient f0(w). Control variates are a classical technique for reducing the variance of a stochastic quantity without introducing bias. Say we have some random variable X.
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs.
Bayesian Batch Active Learning as Sparse Subset Approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick, Josรฉ Miguel Hernรกndez-Lobato
However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change.
UnsupervisedLearningofShapeandPose withDifferentiablePointClouds
We live in a three-dimensional world, and a proper understanding of its volumetric structure is crucial for acting and planning. However, we perceive the world mainly via its two-dimensional projections.
