Search Results for author:
Found 27 papers, 9 papers with code
Duo Streamers: A Streaming Gesture Recognition Framework
Gesture recognition in resource-constrained scenarios faces significant challenges in achieving high accuracy and low latency.
Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features
Melody preservation is crucial in singing voice conversion (SVC).
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement
Based on a masked generative model, AnyEnhance is capable of handling both speech and singing voices, supporting a wide range of enhancement tasks including denoising, dereverberation, declipping, super-resolution, and target speaker extraction, all simultaneously and without fine-tuning.
Impact of genetic polymorphisms on tacrolimus concentrations and intra-individual variability in recipients of heart transplants during the early post-heart transplantation period
CYP3A5*1D and CYP3A7 rs776744 haplotypes correlated significantly with an intra-individual coefficient of variation of tacrolimus trough concentration during the early postoperative period.
Diverse Generation while Maintaining Semantic Coordination: A Diffusion-Based Data Augmentation Method for Object Detection
Recent studies emphasize the crucial role of data augmentation in enhancing the performance of object detection models.
68-Channel Highly-Integrated Neural Signal Processing PSoC with On-Chip Feature Extraction, Compression, and Hardware Accelerators for Neuroprosthetics in 22nm FDSOI
Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments.
Validating Climate Models with Spherical Convolutional Wasserstein Distance
The validation of global climate models is crucial to ensure the accuracy and efficacy of model output.
Learning Interpretable Rules for Scalable Data Representation and Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis.
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling
Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document.
Cloud removal Using Atmosphere Model
Cloud removal is an essential task in remote sensing data analysis.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection
Inspired by the fact that depth quality is a key factor influencing the accuracy, we propose an efficient depth quality-inspired feature manipulation (DQFM) process, which can dynamically filter depth features according to depth quality.
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC).
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.
A Simplified System Model for Optical Camera Communication
Data rate and communication distance are two important criteria for measuring the performance of optical camera communication (OCC) systems.
Scalable Rule-Based Representation Learning for Interpretable Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection
To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning
Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions.
Robotics
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure.
ColluEagle: Collusive review spammer detection using Markov random fields
Previous works use review network effects, i. e. the relationships among reviewers, reviews, and products, to detect fake reviews or review spammers, but ignore time effects, which are critical in characterizing group spamming.
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost.
PACT: Parameterized Clipping Activation for Quantized Neural Networks
We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets.
Efficient Neural Codes under Metabolic Constraints
The advantage could be as large as one-fold, substantially larger than the previous estimation.
Optimization and analysis of large scale data sorting algorithm based on Hadoop
If the data is also too big, it will turn back to the first round and keep on.
Distributed, Parallel, and Cluster Computing
Optimal Neural Population Codes for High-dimensional Stimulus Variables
We consider solutions for a minimal case where the number of neurons in the population is equal to the number of stimulus dimensions (diffeomorphic).
Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum L_p Loss
In this manner, we show how the optimal tuning curve depends upon the loss function, and the equivalence of maximizing mutual information with minimizing $L_p$ loss in the limit as $p$ goes to zero.
