Search Results for author:
Found 23 papers, 4 papers with code
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
The biological neural systems evolved to adapt to ecological environment for efficiency and effectiveness, wherein neurons with heterogeneous structures and rich dynamics are optimized to accomplish complex cognitive tasks.
SongDriver2: Real-time Emotion-based Music Arrangement with Soft Transition
Existing studies mainly focus on achieving emotion real-time fit, while the issue of soft transition remains understudied, affecting the overall emotional coherence of the music.
Variational Relational Point Completion Network for Robust 3D Classification
Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details.
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation
To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation.
Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
Adaptive regularization methods that exploit more than the diagonal entries exhibit state of the art performance for many tasks, but can be prohibitive in terms of memory and running time.
A Nonstochastic Control Approach to Optimization
Selecting the best hyperparameters for a particular optimization instance, such as the learning rate and momentum, is an important but nonconvex problem.
Adaptive Online Learning of Quantum States
In the fundamental problem of shadow tomography, the goal is to efficiently learn an unknown $d$-dimensional quantum state using projective measurements.
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU.
Provable Regret Bounds for Deep Online Learning and Control
The theory of deep learning focuses almost exclusively on supervised learning, non-convex optimization using stochastic gradient descent, and overparametrized neural networks.
Robust Online Control with Model Misspecification
We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification.
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents
Adapting the idea of training CartPole with Deep Q-learning agent, we are able to find a promising result that prevent the pole from falling down.
Unsupervised 3D Shape Completion through GAN Inversion
In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time.
Variational Relational Point Completion Network
In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds.
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.
[Re] Replication Study of 'Generative causal explanations of black-box classifiers'
The authors provide code for most of the experiments presented in the paper.
Label Representations in Modeling Classification as Text Generation
Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate.
Black-Box Control for Linear Dynamical Systems
To complete the picture, we investigate the complexity of the online black-box control problem, and give a matching lower bound of $2^{\Omega(\mathcal{L})}$ on the regret, showing that the additional exponential cost is inevitable.
Online Agnostic Boosting via Regret Minimization
Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules.
Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing
We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.
Cosmology and Nongalactic Astrophysics
Calibration, Entropy Rates, and Memory in Language Models
Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing.
Extreme Tensoring for Low-Memory Preconditioning
State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption.
Efficient Full-Matrix Adaptive Regularization
Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive.
Online Learning of Quantum States
Even in the "non-realizable" setting---where there could be arbitrary noise in the measurement outcomes---we show how to output hypothesis states that do significantly worse than the best possible states at most $\operatorname{O}\!\left(\sqrt {Tn}\right) $ times on the first $T$ measurements.
