Search Results for author:
Found 409 papers, 150 papers with code
Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects
Therefore, it is necessary to develop efficient assembly-independent methods for accurate estimation of these genomic characteristics.
Spatial-Spectral Boosting Analysis for Stroke Patients' Motor Imagery EEG in Rehabilitation Training
Current studies about motor imagery based rehabilitation training systems for stroke subjects lack an appropriate analytic method, which can achieve a considerable classification accuracy, at the same time detects gradual changes of imagery patterns during rehabilitation process and disinters potential mechanisms about motor function recovery.
Simplex-Based 3D Spatio-Temporal Feature Description for Action Recognition
In addition, the results show that our SOD descriptor is a superior individual descriptor for action recognition.
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images
Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images.
HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.
Ranked #174 on
Image Classification
on CIFAR-100
Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey
Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method for various clinical tasks.
Automatic Photo Adjustment Using Deep Neural Networks
Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics.
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget.
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing
Task selection (picking an appropriate labeling task) and worker selection (assigning the labeling task to a suitable worker) are two major challenges in task assignment for crowdsourcing.
Online Markov decision processes with policy iteration
The online Markov decision process (MDP) is a generalization of the classical Markov decision process that incorporates changing reward functions.
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines
To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks.
On the Reducibility of Submodular Functions
The scalability of submodular optimization methods is critical for their usability in practice.
Space-Time Representation of People Based on 3D Skeletal Data: A Review
Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area.
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs).
Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking
Sparse representation has been widely studied in visual tracking, which has shown promising tracking performance.
Self-Reflective Risk-Aware Artificial Cognitive Modeling for Robot Response to Human Behaviors
In order for cooperative robots ("co-robots") to respond to human behaviors accurately and efficiently in human-robot collaboration, interpretation of human actions, awareness of new situations, and appropriate decision making are all crucial abilities for co-robots.
Learning Concept Taxonomies from Multi-modal Data
We study the problem of automatically building hypernym taxonomies from textual and visual data.
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.
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.
Recurrent Topic-Transition GAN for Visual Paragraph Generation
The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators.
Generative Adversarial Network
Image Paragraph Captioning
+1
ZM-Net: Real-time Zero-shot Image Manipulation Network
We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones).
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN.
Ranked #2 on
Image-to-Image Translation
on Aerial-to-Map
Multisensory Omni-directional Long-term Place Recognition: Benchmark Dataset and Analysis
We introduce a new dataset Multisensory Omnidirectional Long-term Place recognition (MOLP) comprising omnidirectional intensity and disparity images.
Robotics
GRASS: Generative Recursive Autoencoders for Shape Structures
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.
Generative Semantic Manipulation with Contrasting GAN
Generative Adversarial Networks (GANs) have recently achieved significant improvement on paired/unpaired image-to-image translation, such as photo$\rightarrow$ sketch and artist painting style transfer.
Ranked #4 on
Facial Expression Translation
on CelebA
Mining Deep And-Or Object Structures via Cost-Sensitive Question-Answer-Based Active Annotations
The loss is defined for nodes in all layers of the AOG, including the generative loss (measuring the likelihood of the images) and the discriminative loss (measuring the fitness to human answers).
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system.
Efficient and Effective Single-Document Summarizations and A Word-Embedding Measurement of Quality
We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.
Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designated semantics or structures.
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences of variable lengths, trees, and graphs.
High-throughput, high-resolution registration-free generated adversarial network microscopy
We combine generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV).
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and autoencoding variational Bayes.
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features.
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e. g., meso-skeletons and surfaces, partial and complete scans, etc.
On the Selection of Anchors and Targets for Video Hyperlinking
A principle way of hyperlinking can be carried out by picking centers of clusters as anchors and from there reach out to targets within or outside of clusters with consideration of neighborhood complexity.
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision.
Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances
To tackle these two challenges, in this paper we propose a novel image representation learning method that can integrate the local patches (the instances) of an input image (the bag) and its holistic representation into one single-vector representation.
Spin-Orbit Protection of Induced Superconductivity in Majorana Nanowires
Spin-orbit interaction (SOI) plays a key role in creating Majorana zero modes in semiconductor nanowires proximity coupled to a superconductor.
Mesoscale and Nanoscale Physics
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
Fast and Accurate Reordering with ITG Transition RNN
Compared to a strong baseline of attention-based RNN, our ITG RNN re-ordering model can reach the same reordering accuracy with only 1/10 of the training data and is 2. 5x faster in decoding.
SketchyScene: Richly-Annotated Scene Sketches
We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level.
Generative Semantic Manipulation with Mask-Contrasting GAN
Despite the promising results on paired/unpaired image-to-image translation achieved by Generative Adversarial Networks (GANs), prior works often only transfer the low-level information (e. g. color or texture changes), but fail to manipulate high-level semantic meanings (e. g., geometric structure or content) of different object regions.
UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text.
Dual-label Deep LSTM Dereverberation For Speaker Verification
In this paper, we present a reverberation removal approach for speaker verification, utilizing dual-label deep neural networks (DNNs).
Semantic WordRank: Generating Finer Single-Document Summarizations
We present Semantic WordRank (SWR), an unsupervised method for generating an extractive summary of a single document.
SCORES: Shape Composition with Recursive Substructure Priors
The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss.
High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants
We present accurate fits for the remnant properties of generically precessing binary black holes, trained on large banks of numerical-relativity simulations.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena
AutoLoss: Learning Discrete Schedule for Alternate Optimization
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.
Hartley Spectral Pooling for Deep Learning
In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size.
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates.
Towards Verifying Semantic Roles Co-occurrence
Semantic role theory considers roles as a small universal set of unanalyzed entities.
Event Representation through Semantic Roles: Evaluation of Coverage
Semantic role theory is a widely used approach for event representation.
Deep Poisson gamma dynamical systems
We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies.
Nearly-tight bounds on linear regions of piecewise linear neural networks
The developments of deep neural networks (DNN) in recent years have ushered a brand new era of artificial intelligence.
CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition
Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks.
Symbolic Graph Reasoning Meets Convolutions
To cooperate with local convolutions, each SGR is constituted by three modules: a) a primal local-to-semantic voting module where the features of all symbolic nodes are generated by voting from local representations; b) a graph reasoning module propagates information over knowledge graph to achieve global semantic coherency; c) a dual semantic-to-local mapping module learns new associations of the evolved symbolic nodes with local representations, and accordingly enhances local features.
Ranked #81 on
Semantic Segmentation
on ADE20K val
Learning Implicit Fields for Generative Shape Modeling
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
Interpretable Complex-Valued Neural Networks for Privacy Protection
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features.
Alternating Synthetic and Real Gradients for Neural Language Modeling
Empirically, we demonstrate the effectiveness of alternating training with synthetic and real gradients after periodic warm restarts on language modeling tasks.
Can learning from natural image denoising be used for seismic data interpolation?
We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation.
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation.
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
The unsupervised BAE-NET is trained with a collection of un-segmented shapes, using a shape reconstruction loss, without any ground-truth labels.
DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input
We propose a real-time DNN-based technique to segment hand and object of interacting motions from depth inputs.
A Seft-adaptive Multicellular GEP Algorithm Based On Fuzzy Control For Function Optimization
According to the concentration and dispersion of individual fitness values in population, the crossover rate, mutation rate and real number set mutation rate of genetic operation are dynamically adjusted.
A Hybrid Precipitation Prediction Method based on Multicellular Gene Expression Programming
Prompt and accurate precipitation forecast is very important for development management of regional water resource, flood disaster prevention and people's daily activity and production plan; however, non-linear and nonstationary characteristics of precipitation data and noise seriously affect forecast accuracy.
DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition
We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
Low Resource Named Entity Recognition
named-entity-recognition
+2
VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning
To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network.
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images.
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.
Next steps of quantum transport in Majorana nanowire devices
Majorana zero modes are localized quasiparticles that obey non-Abelian exchange statistics.
Mesoscale and Nanoscale Physics
Neural Models of Text Normalization for Speech Applications
One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS).
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
We define two types of entropy-based metrics, i. e. (1) the discarding of pixel-wise information used in the forward propagation, and (2) the uncertainty of the input reconstruction, to measure input information contained by a specific layer from two perspectives.
Improving Performance of End-to-End ASR on Numeric Sequences
This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e. g.
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition
We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
An End-to-End Neural Network for Image Cropping by Learning Composition from Aesthetic Photos
In this paper, we primarily focus on improving the accuracy of automatic image cropping, and on further exploring its potential in public datasets with high efficiency.
SDM-NET: Deep Generative Network for Structured Deformable Mesh
At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring a coherence between global shape structure and surface details.
Generating an Overview Report over Many Documents
To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.
Characterize and Transfer Attention in Graph Neural Networks
Does attention matter and, if so, when and how?
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces.
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.
Towards a Unified Evaluation of Explanation Methods without Ground Truth
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges.
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction
We introduce a differential visual similarity metric to train deep neural networks for 3D reconstruction, aimed at improving reconstruction quality.
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.
RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization
The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm.
Robotics Graphics
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem.
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games.
Graph Ordering: Towards the Optimal by Learning
However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Instead of executing translation directly, we steer the translation by requiring the network to produce in-between images that resemble weighted hybrids between images from the input domains.
Propagating Asymptotic-Estimated Gradients for Low Bitwidth Quantized Neural Networks
Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1-3 bits weights and activations.
Developing a Recommendation Benchmark for MLPerf Training and Inference
Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience.
Deep Quaternion Features for Privacy Protection
We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.
Dual-discriminator GAN: A GAN way of profile face recognition
A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases.
Simultaneous Learning from Human Pose and Object Cues for Real-Time Activity Recognition
Real-time human activity recognition plays an essential role in real-world human-centered robotics applications, such as assisted living and human-robot collaboration.
Ocean Reverberation Suppression via Matrix Completion with Sensor Failure
Therefore, it is a challenge for target detection in the ocean reverberation with sensor failure.
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection
There are lots of sensors in computer rooms for the DC monitoring system, and they are inherently related.
Graph2Plan: Learning Floorplan Generation from Layout Graphs
The core component of our learning framework is a deep neural network, Graph2Plan, which converts a layout graph, along with a building boundary, into a floorplan that fulfills both the layout and boundary constraints.
An Unsupervised Semantic Sentence Ranking Scheme for Text Documents
It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph.
Span-based Localizing Network for Natural Language Video Localization
Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model.
Rotation-Equivariant Neural Networks for Privacy Protection
Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.
Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage
Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models.
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
We show results of simultaneous motion and part predictions from synthetic and real scans of 3D objects exhibiting a variety of part mobilities, possibly involving multiple movable parts.
Predictive and Generative Neural Networks for Object Functionality
Given a 3D object in isolation, our functional similarity network (fSIM-NET), a variation of the triplet network, is trained to predict the functionality of the object by inferring functionality-revealing interaction contexts.
Building Interpretable Interaction Trees for Deep NLP Models
This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing.
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering
Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations.
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network
To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges.
PIE-NET: Parametric Inference of Point Cloud Edges
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data.
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind.
COALESCE: Component Assembly by Learning to Synthesize Connections
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections.
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
Some recent schedulers choose job resources for users, but do so without awareness of how DL training can be re-optimized to better utilize the provided resources.
TAP-Net: Transport-and-Pack using Reinforcement Learning
Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search.
Deep N-ary Error Correcting Output Codes
To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC.
Compressive spectral image classification using 3D coded convolutional neural network
The accuracy of classification is effectively improved by exploiting the synergy between the deep learning network and coded apertures.
Interpreting and Boosting Dropout from a Game-Theoretic View
This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions.
Dictionary Learning with Low-rank Coding Coefficients for Tensor Completion
In this paper, we propose a novel tensor learning and coding model for third-order data completion.
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates.
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity.
Context Modeling with Evidence Filter for Multiple Choice Question Answering
In addition to the effective reduction of human efforts of our approach compared, through extensive experiments on OpenbookQA, we show that the proposed approach outperforms the models that use the same backbone and more training data; and our parameter analysis also demonstrates the interpretability of our approach.
Interpreting Multivariate Shapley Interactions in DNNs
In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN.
TM-NET: Deep Generative Networks for Textured Meshes
We introduce a conditional autoregressive model for texture generation, which can be conditioned on both part geometry and textures already generated for other parts to achieve texture compatibility.
Graphics
A Novel Self-Packaged DBBPF With Multiple TZs for 5G Applications
This DBBPF is excited by a pair of U-shape feed lines, which are designed on G6 to fully excite the resonators and to introduce source/load TZs at the same time.
Technical Note: Game-Theoretic Interactions of Different Orders
In this study, we define interaction components of different orders between two input variables based on game theory.
Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images
The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics.
Role Taxonomy of Units in Deep Neural Networks
Identifying the role of network units in deep neural networks (DNNs) is critical in many aspects including giving understandings on the mechanisms of DNNs and building basic connections between deep learning and neuroscience.
Semi-supervised URL Segmentation with Recurrent Neural NetworksPre-trained on Knowledge Graph Entities
Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search.
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles.
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Currently, 3D-FRONT contains 18, 968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets.
Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search.
Bidirectional Convolutional Poisson Gamma Dynamical Systems
Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning
Synchronization is a key step in data-parallel distributed machine learning (ML).
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation
Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention.
Ranked #17 on
Panoptic Segmentation
on COCO test-dev
D$^2$IM-Net: Learning Detail Disentangled Implicit Fields from Single Images
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features.
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution.
On Learning the Right Attention Point for Feature Enhancement
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e. g., classification and segmentation.
GDPNet: Refining Latent Multi-View Graph for Relation Extraction
Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence.
Ranked #4 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
Unsupervised Image Segmentation using Mutual Mean-Teaching
Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision.
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
During testing, a style code is fed into the generator to condition the refinement.
Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception
In this paper, we propose a novel approach to collaborative multi-robot perception that simultaneously integrates view selection, feature selection, and object recognition into a unified regularized optimization formulation, which uses sparsity-inducing norms to identify the robots with the most representative views and the modalities with the most discriminative features.
Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses
This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships.
An Embarrassingly Simple Model for Dialogue Relation Extraction
Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue.
Ranked #9 on
Dialog Relation Extraction
on DialogRE
Towards Understanding and Improving Dropout in Game Theory
Experimental results on various DNNs and datasets have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.
Reinforcement Learning for Flexibility Design Problems
Flexibility design problems are a class of problems that appear in strategic decision-making across industries, where the objective is to design a ($e. g.$, manufacturing) network that affords flexibility and adaptivity.
Data Association Between Perception and V2V Communication Sensors
The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications.
Decision Making
Robotics
Systems and Control
Systems and Control
Dynamic Heterogeneity, Cooperative Motion, and Johari-Goldstein $β$-Relaxation in a Metallic Glass-Forming Material Exhibiting a Fragile to Strong Transition
We investigate the Johari-Goldstein (JG) $\beta$-relaxation process in a model metallic glass-forming (GF) material (Al90Sm10), previously studied extensively by both frequency-dependent mechanical measurements and simulation studies devoted to equilibrium properties, by molecular dynamics simulations based on validated and optimized interatomic potentials with the primary aim of better understanding the nature of this universal relaxation process from a dynamic heterogeneity (DH) perspective.
Materials Science
Large zero-bias peaks in InSb-Al hybrid semiconductor-superconductor nanowire devices
We report electron transport studies on InSb-Al hybrid semiconductor-superconductor nanowire devices.
Mesoscale and Nanoscale Physics
Fast Dynamics in a Model Metallic Glass-forming Material
We investigate the fast $\beta$- and Johari-Goldstein (JG) $\beta$-relaxation processes, along with the elastic scattering response of glass-forming (GF) liquids and the Boson peak, in a simulated Al-Sm GF material exhibiting a fragile-strong (FS) transition.
Materials Science
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models
With this key idea, we design TeraPipe, a high-performance token-level pipeline parallel algorithm for synchronous model-parallel training of Transformer-based language models.
Natural Language Video Localization: A Revisit in Span-based Question Answering Framework
Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem.
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing
To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement.
Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach
Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC).
Memory-Efficient Network for Large-scale Video Compressive Sensing
With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement.
In-plane selective area InSb-Al nanowire quantum networks
Strong spin-orbit semiconductor nanowires coupled to a superconductor are predicted to host Majorana zero modes.
Mesoscale and Nanoscale Physics
Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation
We show that by investigating the feature entropy of units on only training data, it could give discrimination between networks with different generalization ability from the view of the effectiveness of feature representations.
Learning Multiscale Correlations for Human Motion Prediction
We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3. 6M and CMU motion capture dataset.
Estimating the Generalization in Deep Neural Networks via Sparsity
By training DNNs with a wide range of generalization gap on popular datasets, we show that our key quantities and linear model could be efficient tools for estimating the generalization gap of DNNs.
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
Contrastive Attraction and Contrastive Repulsion for Representation Learning
We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples.
Understanding Deep MIMO Detection
We demonstrate that data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires enough training samples to converge in time-varying channels.
Video Corpus Moment Retrieval with Contrastive Learning
We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR.
Parallel Attention Network with Sequence Matching for Video Grounding
In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-modal representation learning, and target moment boundary prediction.
Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously.
SDNet: mutil-branch for single image deraining using swin
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks
Moreover, a novel loss function that combines the center loss and the cross entropy loss is exploited to learn both discriminative and separable features in order to further improve the classification performance.
D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features.
Interventional Video Grounding with Dual Contrastive Learning
2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations.
Neural Marching Cubes
To tackle these challenges, we re-cast MC from a deep learning perspective, by designing tessellation templates more apt at preserving geometric features, and learning the vertex positions and mesh topologies from training meshes, to account for contextual information from nearby cubes.
Learning Mesh Representations via Binary Space Partitioning Tree Networks
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built over a set of planes, where the planes and convexes are both defined by learned network weights.
PhotoChat: A Human-Human Dialogue Dataset with Photo Sharing Behavior for Joint Image-Text Modeling
Based on this dataset, we propose two tasks to facilitate research on image-text modeling: a photo-sharing intent prediction task that predicts whether one intends to share a photo in the next conversation turn, and a photo retrieval task that retrieves the most relevant photo according to the dialogue context.
Ranked #5 on
Image Retrieval
on PhotoChat
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks.
Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network
There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging.
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
This workshop pays a special interest in theoretic foundations, limitations, and new application trends in the scope of XAI.
COSY: COunterfactual SYntax for Cross-Lingual Understanding
Pre-trained multilingual language models, e. g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance.
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering
As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.
Token Shift Transformer for Video Classification
It is worth noticing that our TokShift transformer is a pure convolutional-free video transformer pilot with computational efficiency for video understanding.
Interpreting Attributions and Interactions of Adversarial Attacks
This paper aims to explain adversarial attacks in terms of how adversarial perturbations contribute to the attacking task.
Automatic Modulation Classification Using Involution Enabled Residual Networks
Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications.
Small Object Detection Based on Modified FSSD and Model Compression
Small objects have relatively low resolution, the unobvious visual features which are difficult to be extracted, so the existing object detection methods cannot effectively detect small objects, and the detection speed and stability are poor.
Detecting Small Objects in Thermal Images Using Single-Shot Detector
In this paper, we proposed DDSSD (Dilation and Deconvolution Single Shot Multibox Detector), an enhanced SSD with a novel feature fusion module which can improve the performance over SSD for small object detection.
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
Truecasing is the task of restoring the correct case (uppercase or lowercase) of noisy text generated either by an automatic system for speech recognition or machine translation or by humans.
Edge Partition Modulated Graph Convolutional Networks
In this paper, we introduce a relational graph generative process to model how the observed edges are generated by aggregating the node interactions over multiple overlapping node communities, each of which represents a particular type of relation that contributes to the edges via a logical OR mechanism.
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space.
Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure
As a result, clinicians cannot easily or rapidly scrutinize the CDSS recommendation when facing a difficult diagnosis or treatment decision in practice.
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation
In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects.
Towards Debiasing Temporal Sentence Grounding in Video
In this paper, we propose two debiasing strategies, data debiasing and model debiasing, to "force" a TSGV model to capture cross-modal interactions.
Discovering and Explaining the Representation Bottleneck of DNNs
This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of interactions between input variables encoded in DNNs.
Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation
Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response.
