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Found 20 papers, 7 papers with code
Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine
The former fits DW images from diverse acquisition settings into diffusion tensor field, while the latter applies a deep learning-based denoiser to regularize the diffusion tensor field instead of the DW images, which is free from the limitation of fixed-channel assignment of the network.
ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation
Compared to previous systems, which often require hardware customization according to different robots, our single system can generalize to humanoid hands, arm-hands, arm-gripper, and quadruped-gripper systems with high-precision teleoperation.
Diffusion Model for Planning: A Systematic Literature Review
Diffusion models, which leverage stochastic processes to capture complex data distributions effectively, have shown their performance as generative models, achieving notable success in image-related tasks through iterative denoising processes.
Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR
People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments.
Automatic Adaptation Rule Optimization via Large Language Models
Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response.
Open-TeleVision: Teleoperation with Immersive Active Visual Feedback
Teleoperation serves as a powerful method for collecting on-robot data essential for robot learning from demonstrations.
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network
This work is an improved system that we submitted to task 1 of DCASE2023 challenge.
Fully Few-shot Class-incremental Audio Classification Using Expandable Dual-embedding Extractor
It's assumed that training data is sufficient in base session of few-shot class-incremental audio classification.
Decoupled Alignment for Robust Plug-and-Play Adaptation
We introduce a low-resource safety enhancement method for aligning large language models (LLMs) without the need for supervised fine-tuning (SFT) or reinforcement learning from human feedback (RLHF).
Large Language Models Synergize with Automated Machine Learning
Specifically, our goal is to fully automate the generation and optimization of the code of the entire ML workflow, from data preparation to modeling and post-processing, utilizing only textual descriptions of the ML tasks.
Language Evolution for Evading Social Media Regulation via LLM-based Multi-agent Simulation
Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions.
Exploring the Improvement of Evolutionary Computation via Large Language Models
Evolutionary computation (EC), as a powerful optimization algorithm, has been applied across various domains.
Multi-role Consensus through LLMs Discussions for Vulnerability Detection
Recent advancements in large language models (LLMs) have highlighted the potential for vulnerability detection, a crucial component of software quality assurance.
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks
Dynamic graphs are extensively employed for detecting anomalous behavior in nodes within the Internet of Things (IoT).
Lightweight Speaker Verification Using Transformation Module with Feature Partition and Fusion
The features that are output from current block of the model are processed according to the steps above before they are fed into the next block of the model.
Few-shot Class-incremental Audio Classification Using Stochastic Classifier
It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only.
Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes
Labeled support samples and unlabeled query samples are used to train the prototype adaptation network and update the classifier, since they are informative for audio classification.
Value Iteration Networks with Gated Summarization Module
We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements: (1) employing an Adaptive Iteration Strategy in the Value Iteration module to reduce the number of iterations, and (2) introducing a Gated Summarization module to summarize the iterative process.
Compressed domain vibration detection and classification for distributed acoustic sensing
Distributed acoustic sensing (DAS) is a novel enabling technology that can turn existing fibre optic networks to distributed acoustic sensors.
DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator
We propose DiffSRL, a dynamic state representation learning pipeline utilizing differentiable simulation that can embed complex dynamics models as part of the end-to-end training.
