Search Results for author:
Found 40 papers, 11 papers with code
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects.
Scalable Attribution of Adversarial Attacks via Multi-Task Learning
In this paper, AAP is defined as the recognition of three signatures, i. e., {\em attack algorithm}, {\em victim model} and {\em hyperparameter}.
Sentence Simplification via Large Language Models
Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning.
PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
Machine Learning (ML) techniques facilitate automating malicious software (malware for short) detection, but suffer from evasion attacks.
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.
SCA: Streaming Cross-attention Alignment for Echo Cancellation
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation.
Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networks
Traditional machine learning techniques have been widely used to establish the trust management systems.
Deep Spatial Domain Generalization
Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space.
Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks
Next, the integration of AI and trust management is developed to optimize the intelligence and security.
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses.
Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis
The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification.
Chinese Idiom Paraphrasing
Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters.
Prompt-Learning for Short Text Classification
In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks.
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning
We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved.
Deep Learning-based Predictive Control of Battery Management for Frequency Regulation
The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation.
Learning Canonical F-Correlation Projection for Compact Multiview Representation
With this framework as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping.
Boosting Mobile CNN Inference through Semantic Memory
Human brains are known to be capable of speeding up visual recognition of repeatedly presented objects through faster memory encoding and accessing procedures on activated neurons.
Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning
Our framework outperforms state-of-the-art algorithms on four benchmark datasets in both zero-shot and generalized zero-shot settings, which demonstrates the effectiveness of spiral learning in learning generalizable and complex correlations.
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations.
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages
The availability of parallel sentence simplification (SS) is scarce for neural SS modelings.
Time-Domain Doppler Biomotion Detections Immune to Unavoidable DC Offsets
In the past decades, continuous Doppler radar sensor-based bio-signal detections have attracted many research interests.
Is It Time to Redefine the Classification Task for Deep Learning Systems?
Many works have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples.
DeepCompress: Efficient Point Cloud Geometry Compression
We propose a more efficient deep learning-based encoder architecture for point clouds compression that incorporates principles from established 3D object detection and image compression architectures.
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification
The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes.
Task Aligned Generative Meta-learning for Zero-shot Learning
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).
[Re] Reproducibility study - Does enforcing diversity in hidden states of LSTM-Attention models improve transparency?
We follow four investigative routes: (i) Replication: we rerun experiments on datasets from the paper in order to replicate the results, and add the results that are missing in the paper; (ii) Code review: we scrutinize the code to validate its correctness; (iii) Evaluation methodology: we extend the set of evaluation metrics used in the paper with the LIME method, in an attempt to resolve inconclusive results; (iv) Generalization to other architectures: we test whether the authorsʼ claims apply to variations of the base model (more complex forms of attention and a BiLSTM encoder).
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.
Chinese Lexical Simplification
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning.
Learning Task-aware Robust Deep Learning Systems
Many works demonstrate that deep learning system is vulnerable to adversarial attack.
Weight-dependent Gates for Network Pruning
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint.
LSBert: A Simple Framework for Lexical Simplification
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence.
Agglomerative Neural Networks for Multi-view Clustering
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.
Lexical Simplification with Pretrained Encoders
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning.
A simple and effective postprocessing method for image classification
Whether it is computer vision, natural language processing or speech recognition, the essence of these applications is to obtain powerful feature representations that make downstream applications completion more efficient.
STTM: A Tool for Short Text Topic Modeling
Along with the emergence and popularity of social communications on the Internet, topic discovery from short texts becomes fundamental to many applications that require semantic understanding of textual content.
Information Retrieval
Controlled Tracking in Urban Terrain: Closing the Loop
We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain.
Signal Processing Systems and Control
Simplex Search Based Brain Storm Optimization
Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm.
Constraints on Primordial Magnetic Fields from Planck combined with the South Pole Telescope CMB B-mode polarization measurements
We use the publicly released SPT B-mode polarization spectrum, along with the temperature and polarization data from the Planck satellite, to derive constraints on the magnitude, the spectral index and the energy scale at which the PMF was generated.
Cosmology and Nongalactic Astrophysics
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models
In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs.
