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Found 17 papers, 5 papers with code
Adversarial Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has become an increasingly important research topic in machine learning.
IMUVIE: Pickup Timeline Action Localization via Motion Movies
Falls among seniors due to difficulties with tasks such as picking up objects pose significant health and safety risks, impacting quality of life and independence.
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation.
FCDM: Sparse-view Sinogram Inpainting with Frequency Domain Convolution Enhanced Diffusion Models
Computed tomography (CT) is an imaging technique that uses X-ray projections from multiple rotation angles to create detailed cross-sectional images, widely used in industrial inspection and medical diagnostics.
UniAV: Unified Audio-Visual Perception for Multi-Task Video Event Localization
Video localization tasks aim to temporally locate specific instances in videos, including temporal action localization (TAL), sound event detection (SED) and audio-visual event localization (AVEL).
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging.
BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks
Most existing dynamic or runtime channel pruning methods have to store all weights to achieve efficient inference which brings extra storage costs.
Depth-Aware Concealed Crop Detection in Dense Agricultural Scenes
In this paper we introduce Concealed Crop Detection (CCD) which extends classic COD to agricultural domains.
Device-Wise Federated Network Pruning
Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources.
Structural Alignment for Network Pruning through Partial Regularization
To mitigate this gap, we first learn a target sub-network during the model training process, and then we use this sub-network to guide the learning of model weights through partial regularization.
A Faster Decentralized Algorithm for Nonconvex Minimax Problems
We prove that our DM-HSGD algorithm achieves stochastic first-order oracle (SFO) complexity of $O(\kappa^3 \epsilon^{-3})$ for decentralized stochastic nonconvex-strongly-concave problem to search an $\epsilon$-stationary point, which improves the exiting best theoretical results.
Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm
Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability.
Exploration and Estimation for Model Compression
In this paper, we focus on the discrimination-aware compression of Convolutional Neural Networks (CNNs).
Learning Better Visual Data Similarities via New Grouplet Non-Euclidean Embedding
However, pair-wise methods involve expensive training costs, while proxy-based methods are less accurate in characterizing the relationships between data points.
Improved Generalization of Heading Direction Estimation for Aerial Filming Using Semi-supervised Regression
We show that by leveraging unlabeled sequences, the amount of labeled data required can be significantly reduced.
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
The first-stage network learns feature representations of the environment using low-level LiDAR statistics and the second-stage network combines those learned features with kinematics data.
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios.
