Search Results for author:
Found 117 papers, 14 papers with code
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue
In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling
The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection
Moreover, based on the speech collection, we proposed a neural network-based frame-by-frame mapping method to recover the speech content by converting from the adversarial speech to the human speech.
Exploiting Auxiliary Data for Offensive Language Detection with Bidirectional Transformers
Our approach introduces domain adaptation (DA) training procedures to ALBERT, such that it can effectively exploit auxiliary data from source domains to improve the OLD performance in a target domain.
Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention
In the slot self-attention layers, we force each slot to involve information from the other k prominent slots and mask the rest out.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Securing Fixed Neural Network Steganography
To deal with this issue, we propose a key-based FNNS scheme to improve the security of the FNNS, where we generate key-controlled perturbations from the FNN for data embedding.
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas.
Trustworthy Representation Learning Across Domains
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.
Leveraging Large Language Models for Pre-trained Recommender Systems
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs.
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs.
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
As Vision Transformers (ViTs) are gradually surpassing CNNs in various visual tasks, one may question: if a training scheme specifically for ViTs exists that can also achieve performance improvement without increasing inference cost?
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
With the proposed integer quantization search, we increase the accuracy of ResNet-18 on ImageNet by 1. 31% points and ResNet-50 by 0. 90% points with equivalent model cost over previous methods.
DRAW: Defending Camera-shooted RAW against Image Manipulation
RAW files are the initial measurement of scene radiance widely used in most cameras, and the ubiquitously-used RGB images are converted from RAW data through Image Signal Processing (ISP) pipelines.
RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection
However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field.
PharmacyGPT: The AI Pharmacist
In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists.
Towards Deep Network Steganography: From Networks to Networks
In this paper, we propose deep network steganography for the covert communication of DNN models.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
ChatGPT has shown to be a strong baseline in many NLP tasks, and we believe it has the potential to improve our model in the task of semantic matching and enhance our model's understanding of food-related concepts and relationships.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.
pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting
However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on.
Towards Speech Dialogue Translation Mediating Speakers of Different Languages
We present a new task, speech dialogue translation mediating speakers of different languages.
Generative Steganographic Flow
In the forward mapping, secret data is hidden in the input latent of Glow model to generate stego images.
Generative Steganography Diffusion
However, existing GAN-based GS methods cannot completely recover the hidden secret data due to the lack of network invertibility, while Flow-based methods produce poor image quality due to the stringent reversibility restriction in each module.
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks
Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks.
Artificial General Intelligence (AGI) for Education
Artificial general intelligence (AGI) has gained global recognition as a future technology due to the emergence of breakthrough large language models and chatbots such as GPT-4 and ChatGPT, respectively.
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models
We developed a pipeline that leverages multiple FMs to facilitate remote sensing image semantic segmentation tasks guided by text prompt, which we denote as Text2Seg.
Instance Segmentation
Segmentation Of Remote Sensing Imagery
+2
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education.
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
For similar sized systems, it is ~4. 3x-4. 5x faster than the Graphcore IPU Bow and is 1. 2x-1. 7x faster and uses 1. 3x-1. 9x less power than the Nvidia A100.
Frame Flexible Network
To fix this issue, we propose a general framework, named Frame Flexible Network (FFN), which not only enables the model to be evaluated at different frames to adjust its computation, but also reduces the memory costs of storing multiple models significantly.
Continual Causal Inference with Incremental Observational Data
We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions.
Steganography of Steganographic Networks
We propose to disguise a steganographic network (termed as the secret DNN model) into a stego DNN model which performs an ordinary machine learning task (termed as the stego task).
Better Generative Replay for Continual Federated Learning
Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data.
Fair Attribute Completion on Graph with Missing Attributes
To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems.
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
Towards Explainable Visual Anomaly Detection
Anomaly detection and localization of visual data, including images and videos, are of great significance in both machine learning academia and applied real-world scenarios.
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising.
Continual Causal Effect Estimation: Challenges and Opportunities
A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns.
TripLe: Revisiting Pretrained Model Reuse and Progressive Learning for Efficient Vision Transformer Scaling and Searching
One promising way to accelerate transformer training is to reuse small pretrained models to initialize the transformer, as their existing representation power facilitates faster model convergence.
Exploring Depth Information for Face Manipulation Detection
As one of the important face features, the face depth map, which has shown to be effective in other areas such as the face recognition or face detection, is unfortunately paid little attention to in literature for detecting the manipulated face images.
Frozen CLIP Model is An Efficient Point Cloud Backbone
This paper introduces Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud learner for directly training high-quality point cloud models with a frozen CLIP model.
Semi-supervised Local Cluster Extraction by Compressive Sensing
Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure.
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
Speech-text based multi-modal training with bidirectional attention for improved speech recognition
To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more unpaired text data by multi-modal training, one needs to address two problems: 1) the synchronicity of feature sampling rates between speech and language (aka text data); 2) the homogeneity of the learned representations from two encoders.
Learning to Immunize Images for Tamper Localization and Self-Recovery
Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered.
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training
Therefore, we analyze the feasibility and potentiality of using the layer freezing technique in sparse training and find it has the potential to save considerable training costs.
Hierarchical Capsule Prediction Network for Marketing Campaigns Effect
In this paper, we provide an in-depth analysis of the underlying parse tree-like structure involved in the effect prediction task and we further establish a Hierarchical Capsule Prediction Network (HapNet) for predicting the effects of marketing campaigns.
Generative Steganography Network
A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media.
Image Generation Network for Covert Transmission in Online Social Network
Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels.
Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness
We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask.
Sustainable AI Processing at the Edge
In particular, we explore the use of processing-in-memory (PIM) approaches, mobile GPU accelerators, and recently released FPGAs, and compare them with novel Racetrack memory PIM.
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework of representing the visual semantics in BNNs such as human functional brain networks (FBNs).
Image Protection for Robust Cropping Localization and Recovery
Existing image cropping detection schemes ignore that recovering the cropped-out contents can unveil the purpose of the behaved cropping attack.
A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion
To address this issue, we present a new video watermarking based on joint Dual-Tree Cosine Wavelet Transformation (DTCWT) and Singular Value Decomposition (SVD), which is resistant to frame rate conversion.
Multimodal Fake News Detection via CLIP-Guided Learning
The results indicate that the proposed framework has a better capability in mining crucial features for fake news detection.
Fusion of Self-supervised Learned Models for MOS Prediction
This challenge aims to predict MOS scores of synthetic speech on two tracks, the main track and a more challenging sub-track: out-of-domain (OOD).
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low-resource speech recognition has been long-suffering from insufficient training data.
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials
In our paper, the basket trial is employed as an intuitive example to present this new causal inference setting.
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data
In this paper, we reveal the weaknesses of these strategies, i. e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics.
Invertible Image Dataset Protection
We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models.
GenReg: Deep Generative Method for Fast Point Cloud Registration
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
On Generating Identifiable Virtual Faces
Our virtual face images are visually different from the original ones for privacy protection.
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation
In this paper, we propose a pairwise adversarial training approach to augment training data for unsupervised class-imbalanced domain adaptation.
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
In this paper, we propose a challenging and untouched problem: \textit{Open-Set Single Domain Generalization} (OS-SDG), where target domains include unseen categories out of source label space.
Automated Graph Learning via Population Based Self-Tuning GCN
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
The foremost challenge in treatment effect estimation is how to capture hidden confounders.
Safe reopening of university campuses is possible with COVID-19 vaccination
We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester.
Edge: Enriching Knowledge Graph Embeddings with External Text
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods.
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Searching for Fast Model Families on Datacenter Accelerators
On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency.
Continual Lifelong Causal Effect Inference with Real World Evidence
We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions.
Transferable Feature Learning on Graphs Across Visual Domains
In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains.
Co-embedding of Nodes and Edges with Graph Neural Networks
CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space.
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference.
Collaborative Attention Mechanism for Multi-View Action Recognition
Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions.
Stereotype-Free Classification of Fictitious Faces
The proposed approach aids labeling new data (fictitious output images) by minimizing a penalized version of the least squares cost function between realistic pictures and target pictures.
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles.
A Survey on Causal Inference
Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.
Cross-scale Attention Model for Acoustic Event Classification
However, a potential limitation of the network is that the discriminative features from the bottom layers (which can model the short-range dependency) are smoothed out in the final representation.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning
New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations.
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views.
Learning Robust Data Representation: A Knowledge Flow Perspective
It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch.
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features.
Ranked #1 on
Graph Regression
on Tox21
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
Scene Graph Generation with External Knowledge and Image Reconstruction
Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\etc.
Supervised Transfer Learning for Product Information Question Answering
Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily.
Representation Learning for Treatment Effect Estimation from Observational Data
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
However, existing approaches separate target label optimization and domain-invariant feature learning as different steps.
X-GANs: Image Reconstruction Made Easy for Extreme Cases
Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing.
A Review on Deep Learning Techniques Applied to Answer Selection
Given a question and a set of candidate answers, answer selection is the task of identifying which of the candidates answers the question correctly.
A Simple End-to-End Question Answering Model for Product Information
When evaluating a potential product purchase, customers may have many questions in mind.
Supervised Treebank Conversion: Data and Approaches
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce.
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
Estimating treatment effects from observational data is challenging due to the missing counterfactuals.
Enabling Sparse Winograd Convolution by Native Pruning
Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs.
Parallelizing Word2Vec in Multi-Core and Many-Core Architectures
Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words.
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels.
Parallelizing Word2Vec in Shared and Distributed Memory
In combination, these techniques allow us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge.
Temporal Subspace Clustering for Human Motion Segmentation
Subspace clustering is an effective technique for segmenting data drawn from multiple subspaces.
