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Found 39 papers, 8 papers with code
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
Compared to linear-frequency domain or time-domain speech enhancement, the key advantage of Mel-spectrogram enhancement is that Mel-frequency presents speech in a more compact way and thus is easier to learn, which will benefit both speech quality and ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Leveraging LLM Agents for Automated Optimization Modeling for SASP Problems: A Graph-RAG based Approach
Automated optimization modeling (AOM) has evoked considerable interest with the rapid evolution of large language models (LLMs).
MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert
Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging.
Parametric-ControlNet: Multimodal Control in Foundation Models for Precise Engineering Design Synthesis
Firstly, it handles both partial and complete parametric inputs using a diffusion model that acts as a design autocomplete co-pilot, coupled with a parametric encoder to process the information.
GMS-VINS:Multi-category Dynamic Objects Semantic Segmentation for Enhanced Visual-Inertial Odometry Using a Promptable Foundation Model
To tackle this challenge, we introduce GMS-VINS, which integrates an enhanced SORT algorithm along with a robust multi-category segmentation framework into VIO, thereby improving pose estimation accuracy in environments with diverse dynamic objects and frequent occlusions.
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior sequences.
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models
Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests.
OoDIS: Anomaly Instance Segmentation Benchmark
Autonomous vehicles require a precise understanding of their environment to navigate safely.
Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models
This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset.
Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective
This paper explores the implications of using machine learning models in the pricing of catastrophe (CAT) bonds.
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications.
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR
In this work, we propose Mel-FullSubNet, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion
Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition.
A Novel Field-Free SOT Magnetic Tunnel Junction With Local VCMA-Induced Switching
By integrating the local voltage-controlled magnetic anisotropy (VCMA) effect, Dzyaloshinskii-Moriya interaction (DMI) effect, and spin-orbit torque (SOT) effect, we propose a novel device structure for field-free magnetic tunnel junction (MTJ).
Network Topology Inference with Sparsity and Laplacian Constraints
We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian.
Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning
Federated learning (FL) inevitably confronts the challenge of system heterogeneity in practical scenarios.
End-to-End Lane detection with One-to-Several Transformer
We first propose the one-to-several label assignment, which combines one-to-many and one-to-one label assignment to solve label semantic conflicts while keeping end-to-end detection.
Ranked #28 on
Lane Detection
on CULane
Multi-Exposure HDR Composition by Gated Swin Transformer
Fusing a sequence of perfectly aligned images captured at various exposures, has shown great potential to approach High Dynamic Range (HDR) imaging by sensors with limited dynamic range.
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions
In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications.
CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior
For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression.
A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources.
A Data Quality Assessment Framework for AI-enabled Wireless Communication
Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network.
Speech Dereverberation with a Reverberation Time Shortening Target
The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error.
PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis
A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution
Initially, profit and risk were measured by the first two moments of the portfolio's return, a. k. a.
Speech Dereverberation with A Reverberation Time Shortening Target
The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error.
Sequential Recommendation with Causal Behavior Discovery
The key of sequential recommendation lies in the accurate item correlation modeling.
Transfer Learning for Retinal Vascular Disease Detection: A Pilot Study with Diabetic Retinopathy and Retinopathy of Prematurity
Recently, deep learning techniques have been successfully applied for detection of diabetic retinopathy (DR).
Boundary Distribution Estimation for Precise Object Detection
In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression.
Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge.
A General Framework for Revealing Human Mind with auto-encoding GANs
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.
Searching Personalized $k$-wing in Large and Dynamic Bipartite Graphs
By exploiting the discoveries, we propose novel algorithms for maintaining the two indices, which substantially reduces the cost of maintenance.
Solving High-Order Portfolios via Successive Convex Approximation Algorithms
Investors pursue higher mean and lower variance when designing the portfolios.
Dynamic optimal reinsurance and dividend-payout in finite time horizon
This paper studies a dynamic optimal reinsurance and dividend-payout problem for an insurance company in a finite time horizon.
Early Detection of Retinopathy of Prematurity (ROP) in Retinal Fundus Images Via Convolutional Neural Networks
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight.
Efficient Deep Representation Learning by Adaptive Latent Space Sampling
Supervised deep learning requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
Index-based Solutions for Efficient Density Peak Clustering
Efficient query algorithms are proposed for these indices which significantly avoids irrelevant comparisons at the cost of space.
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks
In addition, a pooling method based on percentile is proposed to improve the accuracy of the model.
Predicting Destinations by a Deep Learning based Approach
However, the standard attention mechanism uses fixed feature representations, and has a limited ability to represent distinct features of locations.
