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Found 60 papers, 17 papers with code
Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction
Modern writing assistance applications are always equipped with a Grammatical Error Correction (GEC) model to correct errors in user-entered sentences.
MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data
We propose scaling up 3D scene reconstruction by training with synthesized data.
A Knowledge-enhanced Pathology Vision-language Foundation Model for Cancer Diagnosis
To derive more nuanced image and text representations, we propose a novel knowledge-enhanced vision-language pre-training approach that integrates disease knowledge into the alignment within hierarchical semantic groups instead of unstructured image-text pairs.
Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy
Building on findings, we proposed ``Script-Strategy Aligned Generation (SSAG)'', a flexible alignment approach that reduces reliance on fully scripted content while enhancing LLMs' therapeutic adherence and controllability.
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
We attribute this issue to the imbalance between the abundance of tunable parameters and the scarcity of labeled molecules, and the lack of contextual perceptiveness in the encoders.
MPT: A Large-scale Multi-Phytoplankton Tracking Benchmark
Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes.
Contrastive Augmentation: An Unsupervised Learning Approach for Keyword Spotting in Speech Technology
To achieve this, we present a speech augmentation-based unsupervised learning method that utilizes the similarity between the bottleneck layer feature and the audio reconstructing information for auxiliary training.
Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies
Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI).
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
This paper addresses the challenge of out-of-distribution (OOD) generalization in graph machine learning, a field rapidly advancing yet grappling with the discrepancy between source and target data distributions.
Perm: A Parametric Representation for Multi-Style 3D Hair Modeling
We present Perm, a learned parametric representation of human 3D hair designed to facilitate various hair-related applications.
EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling
First, we introduce a large-scale benchmark dataset to facilitate deep learning methods.
MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models
In this work, we introduce "MedBench", a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM.
LRM-Zero: Training Large Reconstruction Models with Synthesized Data
We demonstrate that our LRM-Zero, trained with our fully synthesized Zeroverse, can achieve high visual quality in the reconstruction of real-world objects, competitive with models trained on Objaverse.
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning
Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.
Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning
To this end, we introduce Carve3D, an improved RLFT algorithm coupled with a novel Multi-view Reconstruction Consistency (MRC) metric, to enhance the consistency of multi-view diffusion models.
OpenVoice: Versatile Instant Voice Cloning
The voice styles are not directly copied from and constrained by the style of the reference speaker.
GSLB: The Graph Structure Learning Benchmark
To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms.
Impact of COVID-19 Lockdown Measures on Chinese Startups and Local Government Public Finance: Challenges and Policy Implications
This paper aims to assess the impact of COVID-19 on the public finance of Chinese local governments, with a particular focus on the effect of lockdown measures on startups during the pandemic.
All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment
This approach achieves feature integration in a unified backbone, removing the need for carefully-designed fusion modules and resulting in a more effective and efficient VL tracking framework.
Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning
Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer.
MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine
The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts.
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
To address this, we evaluate the quality of propensity scores from the perspective of uncertainty calibration, proposing the use of expected calibration error (ECE) as a measure of propensity-score quality.
PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing
To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections.
GAIT: Generating Aesthetic Indoor Tours with Deep Reinforcement Learning
Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation.
A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions
Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for computation resources of IoT users to support diverse applications.
Joint Optimization of Resource Allocation, Phase Shift and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems
The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT).
A Repulsive Force Unit for Garment Collision Handling in Neural Networks
Despite recent success, deep learning-based methods for predicting 3D garment deformation under body motion suffer from interpenetration problems between the garment and the body.
You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description.
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding.
Abstractive Text Summarization
Grammatical Error Correction
+4
RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects
We test the effectiveness of our representation on the human image harmonization task by predicting shading that is coherent with a given background image.
Relational Triple Extraction: One Step is Enough
Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction.
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification
Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy.
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model
Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns.
Relation-aware Video Reading Comprehension for Temporal Language Grounding
Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence.
SurroundNet: Towards Effective Low-Light Image Enhancement
Although Convolution Neural Networks (CNNs) has made substantial progress in the low-light image enhancement task, one critical problem of CNNs is the paradox of model complexity and performance.
Critical Role of Simultaneous Reduction of Atmospheric Odd Oxygen for Winter Haze Mitigation
The lockdown due to COVID-19 created a rare opportunity to examine the nonlinear responses of secondary aerosols, which are formed through atmospheric oxidation of gaseous precursors, to intensive precursor emission reductions.
M2IOSR: Maximal Mutual Information Open Set Recognition
In this work, we aim to address the challenging task of open set recognition (OSR).
Single-image Full-body Human Relighting
We present a single-image data-driven method to automatically relight images with full-body humans in them.
Alternating Direction Method of Multiplier-Based Distributed Planning Model for Natural Gas, Electricity Network, and Regional Integrated Energy Systems
Simulation results illustrate that a distributed planning model is more sensitive to individual load differences, which is precisely the defect of the joint planning model.
Knowledge Distillation via Instance-level Sequence Learning
Recently, distillation approaches are suggested to extract general knowledge from a teacher network to guide a student network.
Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding
In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC).
Network Embedding via Deep Prediction Model
This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models.
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation
Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation.
Dual Discriminator Adversarial Distillation for Data-free Model Compression
Then the generated samples are used to train the compact student network under the supervision of the teacher.
Similarity Transfer for Knowledge Distillation
The proposed approach promotes the performance of student model as the virtual sample created by multiple images produces a similar probability distribution in the teacher and student networks.
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification
In this paper, we exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.
Open Set Recognition with Conditional Probabilistic Generative Models
A typical challenge that hinders their real-world applications is that unknown samples may be fed into the system during the testing phase, but traditional deep neural networks will wrongly recognize these unknown samples as one of the known classes.
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
Then, we borrow the idea of Coulomb's Law from physics and introduce the concept of attractive force and repulsive force to this graph to learn correlation and mutual exclusion between relations.
Conditional Gaussian Distribution Learning for Open Set Recognition
A typical challenge is that unknown samples may be fed into the system during the testing phase and traditional deep neural networks will wrongly recognize the unknown sample as one of the known classes.
High-Order Paired-ASPP Networks for Semantic Segmenation
The network first introduces a High-Order Representation module to extract the contextual high-order information from all stages of the backbone.
Multi-level Similarity Learning for Low-Shot Recognition
Low-shot learning indicates the ability to recognize unseen objects based on very limited labeled training samples, which simulates human visual intelligence.
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition
An even larger dataset comprising 547 hours of German speech aligned to German text is available for speech recognition.
MIMO Assisted Networks Relying on Intelligent Reflective Surfaces
The network's SE and EE are also derived.
Information Theory Signal Processing Information Theory
Few-shot Learning for Domain-specific Fine-grained Image Classification
Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications.
Highlight Every Step: Knowledge Distillation via Collaborative Teaching
Knowledge distillation aims to train a compact student network by transferring knowledge from a larger pre-trained teacher model.
Non-Rigid Object Tracking via Deformable Patches Using Shape-Preserved KCF and Level Sets
Part-based trackers are effective in exploiting local details of the target object for robust tracking.
Prediction of Sea Surface Temperature using Long Short-Term Memory
We formulate the SST prediction problem as a time series regression problem.
A Procedural Texture Generation Framework Based on Semantic Descriptions
Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture.
