Search Results for author:
Found 517 papers, 159 papers with code
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Clone a voice in 5 seconds to generate arbitrary speech in real-time
W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
In particular, when compared to published models such as conformer-based wav2vec~2. 0 and HuBERT, our model shows~5\% to~10\% relative WER reduction on the test-clean and test-other subsets.
Ranked #1 on
Speech Recognition
on LibriSpeech test-clean
(using extra training data)
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text.
Ranked #2 on
Speech Synthesis
on North American English
WaveGrad: Estimating Gradients for Waveform Generation
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
Ranked #1 on
Speech Recognition
on Hub5'00 SwitchBoard
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.
Conformer: Convolution-augmented Transformer for Speech Recognition
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs).
Ranked #12 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Ranked #32 on
Question Answering
on SQuAD1.1 dev
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
Self-supervised Learning with Random-projection Quantizer for Speech Recognition
In particular the quantizer projects speech inputs with a randomly initialized matrix, and does a nearest-neighbor lookup in a randomly-initialized codebook.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Training RNNs as Fast as CNNs
Common recurrent neural network architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Multi-Task Learning (MTL) has achieved success in various fields.
LibMTL: A Python Library for Multi-Task Learning
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL).
Dual-Balancing for Multi-Task Learning
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields.
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
\textrm{DuReader}_{\textrm{vis}}: A Chinese Dataset for Open-domain Document Visual Question Answering
Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e. g., web pages, PDFs, or Word documents) as the information source.
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2. 1%/4. 6% without external language model (LM), 1. 9%/4. 1% with LM and 2. 9%/7. 0% with only 10M parameters on the clean/noisy LibriSpeech test sets.
Ranked #12 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data.
Ranked #1 on
Dependency Parsing
on CoNLL-2009
Fast and Accurate Neural CRF Constituency Parsing
Estimating probability distribution is one of the core issues in the NLP field.
Ranked #1 on
Constituency Parsing
on CTB7
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Our MetaMath-7B model achieves 66. 4% on GSM8K and 19. 4% on MATH, exceeding the state-of-the-art models of the same size by 11. 5% and 8. 7%.
Ranked #53 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting
We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use.
Sound Audio and Speech Processing
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
An Efficient Person Clustering Algorithm for Open Checkout-free Groceries
Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG.
MATCH: Metadata-Aware Text Classification in A Large Hierarchy
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.
Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks
Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios.
Self-supervised Image Enhancement Network: Training with Low Light Images Only
We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.
Ranked #5 on
Entity Alignment
on YAGO-WIKI50K
Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling
This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor.
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
The model takes an input phoneme sequence, and through an iterative refinement process, generates an audio waveform.
Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification
Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i. e., the domain- specific sentiment words, and pivots, i. e., the domain-shared sentiment words, simultaneously.
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web.
Brain Network Construction and Classification Toolbox (BrainNetClass)
The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods.
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models.
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad.
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature.
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset.
Ranked #1 on
Speech Recognition
on LibriSpeech test-clean
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Dependency Parsing
Semantic Role Labeling (predicted predicates)
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora.
Ranked #1 on
Topic Models
on Arxiv HEP-TH citation graph
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e. g., GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e. g., BERT) have been the prominent choice for natural language understanding (NLU) tasks.
Ranked #5 on
Zero-Shot Text Classification
on AG News
Minimally Supervised Categorization of Text with Metadata
Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity.
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images.
Ranked #4 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Learning an Adaptive Model for Extreme Low-light Raw Image Processing
Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.
Hierarchical Metadata-Aware Document Categorization under Weak Supervision
Our experiments demonstrate a consistent improvement of HiMeCat over competitive baselines and validate the contribution of our representation learning and data augmentation modules.
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Edges in many real-world social/information networks are associated with rich text information (e. g., user-user communications or user-product reviews).
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.
Ranked #4 on
Question Answering
on HotpotQA
Discriminative Topic Mining via Category-Name Guided Text Embedding
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions
In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
Balanced and Hierarchical Relation Learning for One-Shot Object Detection
In this paper, we introduce the balanced and hierarchical learning for our detector.
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).
PEAL: Prior-Embedded Explicit Attention Learning for Low-Overlap Point Cloud Registration
Recently, it has achieved huge success in incorporating Transformer into point cloud feature representation, which usually adopts a self-attention module to learn intra-point-cloud features first, then utilizes a cross-attention module to perform feature exchange between input point clouds.
TLP: A Deep Learning-based Cost Model for Tensor Program Tuning
Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives.
Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention
Audio captioning aims to generate text descriptions of audio clips.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
This leads to the interesting, and \textit{counter-intuitive}, observation that when more antennas are employed by the massive MIMO base station, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length.
Information Theory Signal Processing Information Theory
Attention-guided Chained Context Aggregation for Semantic Segmentation
The way features propagate in Fully Convolutional Networks is of momentous importance to capture multi-scale contexts for obtaining precise segmentation masks.
Ranked #23 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection
LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocity hints.
A Multi-spectral Dataset for Evaluating Motion Estimation Systems
The multi-spectral images, including both color and thermal images in full sensor resolution (640 x 480), are obtained from a standard and a long-wave infrared camera at 32Hz with hardware-synchronization.
Self-supervised Low Light Image Enhancement and Denoising
Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed.
PCR-CG: Point Cloud Registration via Deep Explicit Color and Geometry
Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cloud registration task.
OneLabeler: A Flexible System for Building Data Labeling Tools
In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios.
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set.
Improved Noisy Student Training for Automatic Speech Recognition
Noisy student training is an iterative self-training method that leverages augmentation to improve network performance.
Ranked #5 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-view Self-supervised Disentanglement for General Image Denoising
It is understandable as the model is designed to learn paired mapping (e. g. from a noisy image to its clean version).
Ranked #1 on
Denoising
on CBSD68 sigm75
Deep Reinforcement Learning for Chinese Zero pronoun Resolution
In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models.
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering
Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions.
Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation
Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently.
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task.
Rethinking Training from Scratch for Object Detection
The ImageNet pre-training initialization is the de-facto standard for object detection.
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks.
Personalized Dialogue Generation with Persona-Adaptive Attention
Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona.
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC).
Zero Pronoun Resolution with Attention-based Neural Network
Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents.
Deep Image Clustering with Category-Style Representation
In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure.
Non-autoregressive Text Editing with Copy-aware Latent Alignments
In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments.
Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks
The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion.
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains.
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition
For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personalized Image Semantic Segmentation
In this paper, we address the problem of personalized image segmentation.
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
Discovering latent topics from text corpora has been studied for decades.
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure.
Feature Aggregation and Propagation Network for Camouflaged Object Detection
In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection.
Hierarchical Generative Modeling for Controllable Speech Synthesis
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions.
Training Weakly Supervised Video Frame Interpolation With Events
Though trained on low frame-rate videos, our framework outperforms existing models trained with full high frame-rate videos (and events) on both GoPro dataset and a new real event-based dataset.
E2NeRF: Event Enhanced Neural Radiance Fields from Blurry Images
To solve this problem, we propose a novel Event-Enhanced NeRF (E2NeRF) by utilizing the combination data of a bio-inspired event camera and a standard RGB camera.
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest.
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e. g., category names, category-indicative keywords).
VLLaVO: Mitigating Visual Gap through LLMs
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts.
Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO
This leads to high beam training overhead and loss in the achievable beamforming gains.
Information Theory Signal Processing Information Theory
MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information
We study the problem of weakly supervised text classification, which aims to classify text documents into a set of pre-defined categories with category surface names only and without any annotated training document provided.
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.
Contrastive Graph Learning for Population-based fMRI Classification
Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.
Memory-Efficient Reversible Spiking Neural Networks
In this paper, we propose the reversible spiking neural network to reduce the memory cost of intermediate activations and membrane potentials during training.
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications.
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE).
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
This paper proposed a novel anomaly detection (AD) approach of High-speed Train images based on convolutional neural networks and the Vision Transformer.
MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data
Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house.
Fisher Deep Domain Adaptation
Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification
Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table.
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases.
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
Optical satellite images are a critical data source; however, cloud cover often compromises their quality, hindering image applications and analysis.
Very Deep Convolutional Networks for End-to-End Speech Recognition
Sequence-to-sequence models have shown success in end-to-end speech recognition.
A Self-Training Framework Based on Multi-Scale Attention Fusion for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS) based on image-level labels is challenging since it is hard to obtain complete semantic regions.
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training
Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts.
Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task
We generate pseudo MQM data using parallel data from the WMT translation task.
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
CODAR: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices
Quantum computing devices in the NISQ era share common features and challenges like limited connectivity between qubits.
Quantum Physics
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting
As cameras are increasingly deployed in new application domains such as autonomous driving, performing 3D object detection on monocular images becomes an important task for visual scene understanding.
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection.
Temporal Consistent Automatic Video Colorization via Semantic Correspondence
Extensive experiments demonstrate that our method outperforms other methods in maintaining temporal consistency both qualitatively and quantitatively.
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i. e., those without seed entities).
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors
This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data.
Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware
To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics.
SPColor: Semantic Prior Guided Exemplar-based Image Colorization
Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch.
Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach
However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions.
How to Estimate Model Transferability of Pre-Trained Speech Models?
In this work, we introduce a "score-based assessment" framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks.
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available.
FLEET: Butterfly Estimation from a Bipartite Graph Stream
We consider space-efficient single-pass estimation of the number of butterflies, a fundamental bipartite graph motif, from a massive bipartite graph stream where each edge represents a connection between entities in two different partitions.
Data Structures and Algorithms
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
Improving generalizability of distilled self-supervised speech processing models under distorted settings
Self-supervised learned (SSL) speech pre-trained models perform well across various speech processing tasks.
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image.
Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecasting
To make the model trained on historical data better adapt to future data in a fully online manner, this paper conducts the first study of the online test-time adaptation techniques for spatial-temporal traffic flow forecasting problems.
Ranked #4 on
Traffic Prediction
on PeMS07
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
The proposed feature augmentation strategy can be used in many deep multi-task learning models.
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.
Transforming Visual Scene Graphs to Image Captions
In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs.
A Unifying Framework of Attention-based Neural Load Forecasting
In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction.
Explanation Graph Generation via Generative Pre-training over Synthetic Graphs
The generation of explanation graphs is a significant task that aims to produce explanation graphs in response to user input, revealing the internal reasoning process.
LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery
LEFormer contains three main modules: CNN encoder, Transformer encoder, and cross-encoder fusion.
Selective Partial Domain Adaptation
To solve this problem, we propose a Selective Partial Domain Adaptation (SPDA) method, which selects useful data for the adaptation to the target domain.
Ranked #1 on
Partial Domain Adaptation
on VisDA2017
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion
Entity Set Expansion, Taxonomy Expansion, and Seed-Guided Taxonomy Construction are three representative tasks that can be used to automatically populate an existing taxonomy with new entities.
Effective Structured Prompting by Meta-Learning and Representative Verbalizer
Combining meta-learning the prompt pool and RepVerb, we propose MetaPrompter for effective structured prompting.
UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment
Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science.
Learning to Multitask
Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation.
Image Co-segmentation via Multi-scale Local Shape Transfer
Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category.
Parameter Transfer Unit for Deep Neural Networks
Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures.
Expert Finding in Community Question Answering: A Review
Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance.
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
We propose a novel co-training framework with a distributional module and a pattern module.
Explicablility as Minimizing Distance from Expected Behavior
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop.
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient.
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Then the built user model is used as a leverage to train the agent model by deep reinforcement learning.
Learning Graphical Models from a Distributed Stream
We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors.
A Deep Neural Network for Chinese Zero Pronoun Resolution
This is because zero pronouns have no descriptive information, which results in difficulty in explicitly capturing their semantic similarities with antecedents.
Learning Latent Representations for Speech Generation and Transformation
In this paper, we apply a convolutional VAE to model the generative process of natural speech.
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation
Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities.
Learning to Transfer
We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function.
AI Challenges in Human-Robot Cognitive Teaming
Among the many anticipated roles for robots in the future is that of being a human teammate.
Causes and Corrections for Bimodal Multipath Scanning with Structured Light
Structured light illumination is an active 3-D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface.
Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
In the case of phase measuring profilometry (PMP), the projected patterns are composed of a rolling sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the target surface to remain motionless or for scanning to be performed at such high rates that any movement is small.
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior.
Personalizing a Dialogue System with Transfer Reinforcement Learning
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Very often data we encounter in practice is a collection of matrices rather than a single matrix.
Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts.
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features.
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.
Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator
We introduce the concept of a Visual Compiler that generates a scene specific pedestrian detector and pose estimator without any pedestrian observations.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images.
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.
Proactive Decision Support using Automated Planning
Proactive decision support (PDS) helps in improving the decision making experience of human decision makers in human-in-the-loop planning environments.
Exploit Bounding Box Annotations for Multi-label Object Recognition
With strong labels, our framework is able to achieve state-of-the-art results in both datasets.
Ranked #16 on
Multi-Label Classification
on PASCAL VOC 2007
Neural Recovery Machine for Chinese Dropped Pronoun
Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese, Japanese etc.
Plan Explicability and Predictability for Robot Task Planning
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.
Recurrent Neural Network Encoder with Attention for Community Question Answering
We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks.
Storm Detection by Visual Learning Using Satellite Images
In our system, we extract and summarize important visual storm evidence from satellite image sequences in the way that meteorologists interpret the images.
On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation
We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE).
Highway Long Short-Term Memory RNNs for Distant Speech Recognition
In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers.
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition
In this paper, we investigate the use of prediction-adaptation-correction recurrent neural networks (PAC-RNNs) for low-resource speech recognition.
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data
With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data that need to be jointly analyzed to explore their latent connections.
Weakly Supervised Fine-Grained Image Categorization
Most existing works heavily rely on object / part detectors to build the correspondence between object parts by using object or object part annotations inside training images.
Plan or not: Remote Human-robot Teaming with Incomplete Task Information
While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance.
Learning of Agent Capability Models with Applications in Multi-agent Planning
Thus far, there are two common representations of agent models: MDP based and action based, which are both based on action modeling.
A Formal Analysis of Required Cooperation in Multi-agent Planning
Then, by dividing the problems that require cooperation (referred to as RC problems) into two classes -- problems with heterogeneous and homogeneous agents, we aim to identify all the conditions that can cause RC in these two classes.
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure.
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data
Crowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers.
Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams
We present an efficient distributed online learning scheme to classify data captured from distributed, heterogeneous, and dynamic data sources.
