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Found 31 papers, 3 papers with code
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low resource speech recognition has been long-suffering from insufficient training data.
Fine-Grained Predicates Learning for Scene Graph Generation
The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e. g., "woman-on/standing on/walking on-beach" or "woman-near/looking at/in front of-child".
PM-MMUT: Boosted Phone-mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition
To boost the performance of PMT, we propose multi-modeling unit training (MMUT) architecture fusion with PMT (PM-MMUT).
Feature selection revisited in the single-cell era
Feature selection techniques are essential for high-dimensional data analysis.
Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees
Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems.
Minimum word error training for non-autoregressive Transformer-based code-switching ASR
Non-autoregressive end-to-end ASR framework might be potentially appropriate for code-switching recognition task thanks to its inherent property that present output token being independent of historical ones.
Adaptive Wavelet Transformer Network for 3D Shape Representation Learning
We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition.
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation
In contrast to the PGD-k attack, our method generates adversarial samples that keep the geometric features in clean samples and contain few outliers.
Reasoning over Entity-Action-Location Graph for Procedural Text Understanding
Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.
An Ecological Robustness-Oriented Approach for Power System Network Expansion
Then, we present a mixed-integer nonlinear programming problem (MINLP) that expands the transmission network structure to maximize ecological robustness with power system constraints for an improved ability to absorb disturbances.
G-VAE, a Geometric Convolutional VAE for ProteinStructure Generation
In this work, we introduce a joint geometric-neural networks approach for comparing, deforming and generating 3D protein structures.
Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment
Our ResNet-TW (Deep Residual Network for Time Warping) tackles the alignment problem by compositing a flow of incremental diffeomorphic mappings.
E2E-based Multi-task Learning Approach to Joint Speech and Accent Recognition
In this paper, we propose a single multi-task learning framework to perform End-to-End (E2E) speech recognition (ASR) and accent recognition (AR) simultaneously.
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
RPATTACK: Refined Patch Attack on General Object Detectors
Firstly, we propose a patch selection and refining scheme to find the pixels which have the greatest importance for attack and remove the inconsequential perturbations gradually.
Toward Efficient Wide-Area Identification of Multiple Element Contingencies in Power Systems
Based on each selected branch, the approach constructs the subgraph with parameters of distance and search level, while using branches' LODF metrics as the weights.
Multi-Source Data Fusion for Cyberattack Detection in Power Systems
We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions.
Real-time Power System Simulation with Hardware Devices through DNP3 in Cyber-Physical Testbed
The usage and configuration of DNP3 with real-world equipment in to achieve power system monitoring and control of a large-scale synthetic electric grid via this DNP3 communication is presented.
Mixed-Integer Optimization for Bio-Inspired Robust Power Network Design
This paper presents an approach to address this challenge through bio-inspired power system network design to improve system reliability and resilience against disturbances.
Considerations in the Automatic Development of Electric Grid Restoration Plans
Power system restoration is a highly complex task that must be performed in a timely manner following a blackout.
Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework
Experimental results on an 8-accent English speech recognition show both methods can yield WERs close to the conventional ASR systems that completely ignore the accent, as well as desired AR accuracy.
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence
In this paper, we propose a novel meta-learning based 3D point signature model, named 3Dmetapointsignature (MEPS) network, that is capable of learning robust point signatures in 3D shapes.
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction.
Robust Image Matching By Dynamic Feature Selection
Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching.
Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech
Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre.
Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems
In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance.
Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
We design a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation in order to integrate advantages and avoid limitations of these two categories of approaches.
Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data
However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.
Failure Analysis on Multivariate Time-series Data given Uncertain Labels
Machine failure analysis and detection is critical to today’s industrial society.
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition
Video action recognition, a critical problem in video understanding, has been gaining increasing attention.
Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph
The redundant features existing in high dimensional datasets always affect the performance of learning and mining algorithms.
