Search Results for author:
Found 32 papers, 9 papers with code
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.
Efficient Full-Matrix Adaptive Regularization
Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive.
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.
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.
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
Online Agnostic Boosting via Regret Minimization
Boosting is a widely used machine learning approach based on the idea of aggregating weak learning rules.
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.
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.
[Re] Replication Study of 'Generative causal explanations of black-box classifiers'
The authors provide code for most of the experiments presented in the paper.
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.
Variational Relational Point Completion Network
In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds.
Ranked #2 on
Point Cloud Completion
on Completion3D
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.
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.
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.
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.
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU.
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.
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.
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.
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.
REMAST: Real-time Emotion-based Music Arrangement with Soft Transition
Music as an emotional intervention medium has important applications in scenarios such as music therapy, games, and movies.
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.
Performance Bounds for Near-Field Localization with Widely-Spaced Multi-Subarray mmWave/THz MIMO
By employing the Riemann sum, closed-form CRB expressions are derived for the spherical wavefront-based WSMS (SW-WSMS).
The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models
Despite the impressive performance achieved by pre-trained language-and-vision models in downstream tasks, it remains an open question whether this reflects a proper understanding of image-text interaction.
Robust Learning Based Condition Diagnosis Method for Distribution Network Switchgear
This paper introduces a robust, learning-based method for diagnosing the state of distribution network switchgear, which is crucial for maintaining the power quality for end users.
DesignGPT: Multi-Agent Collaboration in Design
Generative AI faces many challenges when entering the product design workflow, such as interface usability and interaction patterns.
Playing Large Games with Oracles and AI Debate
We give a novel efficient algorithm for internal regret minimization whose regret and per-iteration computation depend logarithmically on the number of actions.
Spectral State Space Models
This paper studies sequence modeling for prediction tasks with long range dependencies.
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
In this paper, we give query and regret optimal bandit algorithms under the strict notion of strongly adaptive regret, which measures the maximum regret over any contiguous interval $I$.
Knowledge Graph Assisted Automatic Sports News Writing
In this paper, we present a novel method for automatically generating sports news, which employs a unique algorithm that extracts pivotal moments from live text broadcasts and uses them to create an initial draft of the news.
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals.
