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Found 40 papers, 16 papers with code
融合目标端句法的AMR-to-Text生成(AMR-to-Text Generation with Target Syntax)
抽象语义表示到文本(AMR-to-Text)生成的任务是给定AMR图, 生成相同语义表示的文本。可以把此任务当作一个从源端AMR图到目标端句子的机器翻译任务。目前存在的一些方法都在探索如何更好的对图结构进行建模。然而, 它们都存在一个未限定的问题, 因为在生成阶段许多句法的决策并不受语义图的约束, 从而忽略了句子内部潜藏的句法信息。为了明确考虑这一不足, 该文提出一种直接而有效的方法, 显示的在AMR-to-Text生成的任务中融入句法信息, 并在Transformer和目前该任务最优性能的模型上进行了实验。实验结果表明, 在现存的两份标准英文数据集LDC2018E86和LDC2017T10上, 都取得了显著的提升, 达到了新的最高性能。
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability
In this setting, considering that self-supervised model could be trained by completely different self-supervised paradigms, e. g., masked image modeling and contrastive learning, with complex training details, we propose a unified membership inference method called PartCrop.
A Two-Stage Framework in Cross-Spectrum Domain for Real-Time Speech Enhancement
Two-stage pipeline is popular in speech enhancement tasks due to its superiority over traditional single-stage methods.
A full-time scale energy management and battery size optimization for off-grid renewable power to hydrogen systems: A battery energy storage-based grid-forming case in Inner Mongolian
For an off-grid renewable power to hydrogen system (OReP2HS), a grid-forming (GFM) source is essential to provide frequency and voltage references.
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment
To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance.
VSANet: Real-time Speech Enhancement Based on Voice Activity Detection and Causal Spatial Attention
The deep learning-based speech enhancement (SE) methods always take the clean speech's waveform or time-frequency spectrum feature as the learning target, and train the deep neural network (DNN) by reducing the error loss between the DNN's output and the target.
Magnitude-and-phase-aware Speech Enhancement with Parallel Sequence Modeling
In speech enhancement (SE), phase estimation is important for perceptual quality, so many methods take clean speech's complex short-time Fourier transform (STFT) spectrum or the complex ideal ratio mask (cIRM) as the learning target.
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition
This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O).
ATM: Action Temporality Modeling for Video Question Answering
Despite significant progress in video question answering (VideoQA), existing methods fall short of questions that require causal/temporal reasoning across frames.
Ranked #17 on
Video Question Answering
on NExT-QA
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts.
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm.
Psychologically-Inspired Music Recommendation System
In the last few years, automated recommendation systems have been a major focus in the music field, where companies such as Spotify, Amazon, and Apple are competing in the ability to generate the most personalized music suggestions for their users.
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization
In the second stage, we propose a simple and effective method for evaluating the confidence of pseudo-labels based on classification discrimination, and by learning consistency from high-quality pseudo-labels, we further refine the localization network to get better localization performance.
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning
In this paper, we proposed Sky Computing, a load-balanced model parallelism framework to adaptively allocate the weights to devices.
Flow-level Coordination of Connected and Autonomous Vehicles in Multilane Freeway Ramp Merging Areas
The strategy is formulated under an optimization framework, where the optimal control plan is determined based on real-time traffic conditions.
Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Review
This paper presents a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.
E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation
In this paper, we notice that the class weights of categories that tend to share many adjacent boundary pixels lack discrimination, thereby limiting the performance.
MSP : Refine Boundary Segmentation via Multiscale Superpixel
Inspired by the generated sharp edges of superpixel blocks, we employ superpixel to guide the information passing within feature map.
Multi-View Stereo with Transformer
It is built upon Transformer and is capable of extracting dense features with global context and 3D consistency, which are crucial to achieving reliable matching for MVS.
Dense Representative Tooth Landmark/axis Detection Network on 3D Model
Results show that our method can produce tooth landmarks with high accuracy.
A Aelf-supervised Tibetan-chinese Vocabulary Alignment Method Based On Adversarial Learning
Tibetan is a low-resource language.
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data
As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models.
Continuous Treatment Recommendation with Deep Survival Dose Response Function
We propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data, which we call the Deep Survival Dose Response Function (DeepSDRF).
Improving Freeway Merging Efficiency via Flow-Level Coordination of Connected and Autonomous Vehicles
Freeway on-ramps are typical bottlenecks in the freeway network due to the frequent disturbances caused by their associated merging, weaving, and lane-changing behaviors.
Efficient Two-Step Networks for Temporal Action Segmentation
Due to boundary ambiguity and over-segmentation issues, identifying all the frames in long untrimmed videos is still challenging.
Ranked #12 on
Action Segmentation
on GTEA
Einstein-Podolsky-Rosen Steering in Two-sided Sequential Measurements with One Entangled Pair
Non-locality sharing amongmultiple observers is predicted and experimentally observed.
Quantum Physics
Diagonalization of Hamiltonian for finite-sized dispersive media: Canonical quantization with numerical mode-decomposition (CQ-NMD)
We present a new math-physics modeling approach, called canonical quantization with numerical mode-decomposition, for capturing the physics of how incoming photons interact with finite-sized dispersive media, which is not describable by the previous Fano-diagonalization methods.
Quantization
Quantum Physics
CDS -- Causal Inference with Deep Survival Model and Time-varying Covariates
Causal inference in longitudinal observational health data often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-varying covariates.
Dynamic prediction of time to event with survival curves
With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource.
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures
Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features.
Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures
In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum.
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis
The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings.
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.
Contribution of Radio Halos to the Foreground for SKA EoR Experiments
The overwhelming foreground contamination is one of the primary impediments to probing the EoR through measuring the redshifted 21 cm signal.
Cosmology and Nongalactic Astrophysics
Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth.
Attending Category Disentangled Global Context for Image Classification
In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance.
Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models
In this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs).
Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors
Building on top of the powerful concept of semantic learning, this paper proposes a Recurrent Binary Embedding (RBE) model that learns compact representations for real-time retrieval.
Deep Embedding Forest: Forest-based Serving with Deep Embedding Features
Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware.
X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM
An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed.
