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Found 24 papers, 8 papers with code
Edit3K: Universal Representation Learning for Video Editing Components
Each video in our dataset is rendered by various image/video materials with a single editing component, which supports atomic visual understanding of different editing components.
JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks
Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy.
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines
Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems.
IoTCO2: Assessing the End-To-End Carbon Footprint of Internet-of-Things-Enabled Deep Learning
To improve privacy and ensure quality-of-service (QoS), deep learning (DL) models are increasingly deployed on Internet of Things (IoT) devices for data processing, significantly increasing the carbon footprint associated with DL on IoT, covering both operational and embodied aspects.
DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines
In this work, we observe that the gradient computation phase during training is a significant bottleneck on GPUs due to the large number of atomic operations that need to be processed.
LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models
The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions.
ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting
In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections.
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
Understanding stochastic gradient descent (SGD) and its variants is essential for machine learning.
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge
Multi-level encoding of internal sentence structures via data-driven is carried out firstly by Transformer, sememes knowledge base HowNet is introduced for knowledge-driven to model the semantic knowledge association among sentence pairs.
QTrojan: A Circuit Backdoor Against Quantum Neural Networks
We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper.
Lower Bounds for Learning in Revealing POMDPs
However, the fundamental limits for learning in revealing POMDPs are much less understood, with existing lower bounds being rather preliminary and having substantial gaps from the current best upper bounds.
Uniform-in-time propagation of chaos for mean field Langevin dynamics
We study the mean field Langevin dynamics and the associated particle system.
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Recent work has identified several tractable subclasses that are learnable with polynomial samples, such as Partially Observable Markov Decision Processes (POMDPs) with certain revealing or decodability conditions.
Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning
Finding unified complexity measures and algorithms for sample-efficient learning is a central topic of research in reinforcement learning (RL).
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature.
Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization
We show that the proposed method converges to the quantal response equilibrium (QRE) -- the equilibrium to the entropy-regularized game -- at a sublinear rate, which is independent of the size of the action space and grows at most sublinearly with the number of agents.
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy
Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem.
Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference
Specifically, we first propose two simple but effective meta-strategies for the box classifier and RPN module to enable the object detection of novel categories with instant response.
Ranked #17 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Estimating Graph Dimension with Cross-validated Eigenvalues
In this situation, one could argue that the correct choice of $k$ is the number of detectable dimensions.
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem into a minimax optimization problem to identify weak solutions.
A New Basis for Sparse Principal Component Analysis
Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a $p \times k$ matrix) is approximately sparse.
Targeted sampling from massive block model graphs with personalized PageRank
Using the degree-corrected stochastic block model, we study whether the PPR vector can select nodes that belong to the same block as the seed node.
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment
However, the power hungry analog-to-digital converters (ADCs) prevent the practical deployment of ReRAM-based DNN accelerators on end devices with limited chip area and power budget.
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics.
