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Found 92 papers, 38 papers with code
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
Robustness Testing of Language Understanding in Task-Oriented Dialog
Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution.
IDRLnet: A Physics-Informed Neural Network Library
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs).
Hyperbolic Deep Neural Networks: A Survey
Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing hierarchical structure.
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclidean structure data.
Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement
The method includes two parts: 1) a Spatio-Temporal Video Enhancement Network (STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal recovery.
Heart rate estimation
Photoplethysmography (PPG) heart rate estimation
+2
BLiMP: The Benchmark of Linguistic Minimal Pairs for English
We introduce The Benchmark of Linguistic Minimal Pairs (shortened to BLiMP), a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English.
MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation
In this paper, we introduce MultiWOZ 2. 3, in which we differentiate incorrect annotations in dialogue acts from dialogue states, identifying a lack of co-reference when publishing the updated dataset.
FNeVR: Neural Volume Rendering for Face Animation
In FNeVR, we design a 3D Face Volume Rendering (FVR) module to enhance the facial details for image rendering.
Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach
It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models.
Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs
We conclude that a variety of methods is necessary to reveal all relevant aspects of a model's grammatical knowledge in a given domain.
Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer
With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years.
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
In this work, we develop a physics-informed neural network-based temperature field inversion (PINN-TFI) method to solve the TFI-HSS task and a coefficient matrix condition number based position selection of observations (CMCN-PSO) method to select optima positions of noise observations.
Data Leakage and Evaluation Issues in Micro-Expression Analysis
Micro-expressions have drawn increasing interest lately due to various potential applications.
Towards General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks
To measure the effectiveness and generalizability of DINOv2's feature representations, we analyze the model across medical image analysis tasks including disease classification and organ segmentation on both 2D and 3D images, and under different settings like kNN, few-shot learning, linear-probing, end-to-end fine-tuning, and parameter-efficient fine-tuning.
metaCAT: A Metadata-based Task-oriented Chatbot Annotation Tool
Creating high-quality annotated dialogue corpora is challenging.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
To address this task, we propose a TOD system with hybrid knowledge management, HyKnow.
Accelerating Physics-Informed Neural Network Training with Prior Dictionaries
Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical guarantee of corresponding error bounds.
Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning
This paper illustrates Huawei’s submission to the WMT20 low-resource parallel corpus filtering shared task.
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection
URR aims to implicitly extract dependencies between utterances, as well as utterances and options, and make reasoning with relational graph convolutional networks.
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks
Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its flexibility and ease of incorporating observed data.
ASR Error Correction with Constrained Decoding on Operation Prediction
Error correction techniques remain effective to refine outputs from automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities
Motivations, emotions, and actions are inter-related essential factors in human activities.
Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation
Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task.
Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings
Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words.
Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks
Physics-informed neural networks (PINNs) have been proposed to solve two main classes of problems: data-driven solutions and data-driven discovery of partial differential equations.
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs.
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
Assessing the Reliability of Large Language Model Knowledge
Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks.
Neural-Symbolic Recommendation with Graph-Enhanced Information
Some researchers use user behavior for logic reasoning to achieve recommendation prediction from the perspective of cognitive reasoning, but this kind of reasoning is a local one and ignores implicit information on a global scale.
When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training
Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances.
CLSEG: Contrastive Learning of Story Ending Generation
To address these two issues, we propose a novel Contrastive Learning framework for Story Ending Generation (CLSEG), which has two steps: multi-aspect sampling and story-specific contrastive learning.
Imputing Brain Measurements Across Data Sets via Graph Neural Networks
An alternative is to impute the missing measurements via a deep learning approach.
Scalable and Efficient Hypothesis Testing with Random Forests
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods.
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework
Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facialexpressions.
Micro Expression Recognition
Micro-Expression Recognition
+1
Training GANs with Centripetal Acceleration
Training generative adversarial networks (GANs) often suffers from cyclic behaviors of iterates.
SPMF: A Social Trust and Preference Segmentation-based Matrix Factorization Recommendation Algorithm
The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas.
An Integrated Autoencoder-Based Filter for Sparse Big Data
We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilizes auxiliary information to mitigate data sparsity.
Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statistics
Random forests remain among the most popular off-the-shelf supervised learning algorithms.
Video Action Recognition Via Neural Architecture Searching
Deep neural networks have achieved great success for video analysis and understanding.
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task
This paper describes Huawei{'}s neural machine translation systems for the WMT 2019 biomedical translation shared task.
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation
Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora.
Applying Cyclical Learning Rate to Neural Machine Translation
In training deep learning networks, the optimizer and related learning rate are often used without much thought or with minimal tuning, even though it is crucial in ensuring a fast convergence to a good quality minimum of the loss function that can also generalize well on the test dataset.
Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition
In this paper, we analyze the influence of learning complexity, including the input complexity and model complexity, and discover that the lower-resolution input data and shallower-architecture model are helpful to ease the degradation of deep models in composite-database task.
Micro Expression Recognition
Micro-Expression Recognition
+1
IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation.
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition
In human action recognition, current works introduce a dynamic graph generation mechanism to better capture the underlying semantic skeleton connections and thus improves the performance.
Ranked #44 on
Skeleton Based Action Recognition
on NTU RGB+D 120
2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework
To address the problem of training on small datasets for action recognition tasks, most prior works are either based on a large number of training samples or require pre-trained models transferred from other large datasets to tackle overfitting problems.
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
A hybrid quantum-classical neural network with deep residual learning
In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed.
IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension
Most of the existing systems design a simple classifier to determine answerability implicitly without explicitly modeling mutual interaction and relation between the question and passage, leading to the poor performance for determining the unanswerable questions.
Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking
Dialogue State Tracking (DST), which is the process of inferring user goals by estimating belief states given the dialogue history, plays a critical role in task-oriented dialogue systems.
Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering
It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy and misleading information.
A novel meta-learning initialization method for physics-informed neural networks
Inspired by this idea, we propose the new Reptile initialization to sample more tasks from the parameterized PDEs and adapt the penalty term of the loss.
TPRM: A Topic-based Personalized Ranking Model for Web Search
Ranking models have achieved promising results, but it remains challenging to design personalized ranking systems to leverage user profiles and semantic representations between queries and documents.
Bi-directional CognitiveThinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona
In this paper, we describe approaches for developing Emily, an emotion-affective open-domain chatbot.
Huawei’s Submissions to the WMT20 Biomedical Translation Task
This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task.
Huawei AARC’s Submissions to the WMT21 Biomedical Translation Task: Domain Adaption from a Practical Perspective
This paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation systems and submissions to the WMT21 biomedical translation shared task.
Modeling Intention, Emotion and External World in Dialogue Systems
Intention, emotion and action are important elements in human activities.
A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain
Second, a physics-driven CNN surrogate with partial differential equation (PDE) residuals as a loss function is utilized for fast meshing (meshing surrogate); then, we present a data-driven surrogate model based on the multi-level reduced-order method, aiming to learn solutions of temperature field in the above regular computational plane (thermal surrogate).
Hyperbolic Uncertainty Aware Semantic Segmentation
Semantic segmentation (SS) aims to classify each pixel into one of the pre-defined classes.
Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks
While most of these methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e. g., Cartesian undersampling or Non-Cartesian sampling, to date, there is limited research aiming to learn and optimize k-space sampling strategies using deep neural networks.
Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition
However, the discriminability of facial landmarks for MER is unclear.
Micro Expression Recognition
Micro-Expression Recognition
+1
CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective
In this paper, we propose a novel cognitive framework of individual interaction.
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
Besides, for inverse PDE problems, problem parameters considered as new output layer weights are unified in a framework with forward PDE problems.
Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System
Specifically, CFO-Net designs a feedback memory module, including strategy pool and feedback pool, to obtain emotion-aware strategy representation.
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously.
Positively transitioned sentiment dialogue corpus for developing emotion-affective open-domain chatbots
In this paper, we describe a data enhancement method for developing Emily, an emotion-affective open-domain chatbot.
Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites
The rapid analysis of thermal stress and deformation plays a pivotal role in the thermal control measures and optimization of the structural design of satellites.
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction
Despite their progress in high-resource language settings, unsupervised bilingual lexicon induction (UBLI) models often fail on corpora with low-resource distant language pairs due to insufficient initialization.
Psychology-guided Controllable Story Generation
Inspired by psychology theories, we introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories.
FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation
The core module in FADO consists of a dual-level feedback strategy selector and a double control reader.
Using Persuasive Writing Strategies to Explain and Detect Health Misinformation
We evaluate fine-tuning and prompt-engineering techniques with pre-trained language models of the BERT family and the generative large language models of the GPT family using persuasive strategies as an additional source of information.
RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
We introduce GNNs into physics-informed learning to better handle irregular domains with unstructured meshes.
Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling
The ablation study also validates that the proposed coordinate sequence decoder and the visual-alignment loss are the keys to the success of our method.
Video Action Recognition with Attentive Semantic Units
Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations.
Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning
We propose a novel Knowledge-Augmented Frame Semantic Parsing Architecture (KAF-SPA) to enhance semantic representation by incorporating accurate frame knowledge into PLMs during frame semantic parsing.
Learning Homographic Disambiguation Representation for Neural Machine Translation
Visualization methods like heatmaps, T-SNE and translation examples are also utilized to demonstrate the effects of the proposed method.
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition
Visible-infrared recognition (VI recognition) is a challenging task due to the enormous visual difference across heterogeneous images.
Generation of 3D Molecules in Pockets via Language Model
Generative models for molecules based on sequential line notation (e. g.
ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition
Visual chart recognition systems are gaining increasing attention due to the growing demand for automatically identifying table headers and values from chart images.
PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer
We present PBFormer, an efficient yet powerful scene text detector that unifies the transformer with a novel text shape representation Polynomial Band (PB).
Modality Unifying Network for Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) is a challenging task due to large cross-modality discrepancies and intra-class variations.
Neuro-Symbolic Recommendation Model based on Logic Query
A recommendation system assists users in finding items that are relevant to them.
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements.
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity.
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition
With the aligned entities, we feed their text embeddings to a transformer-based video adapter as the queries, which can help extract the semantics of the most important entities from a video to a vector.
Large language models in healthcare and medical domain: A review
The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension.
A Survey on Hallucination in Large Vision-Language Models
In this comprehensive survey, we dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation.
