Search Results for author:
Found 133 papers, 54 papers with code
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention.
Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions
To promote the navigational safety of vessels, many computational methods have been presented to perform visual quality enhancement under poor weather conditions.
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition
Furthermore, we propose the serialized speech information guidance SOT (GEncSep) to further utilize the separated encodings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
Improved Esophageal Varices Assessment from Non-Contrast CT Scans
Esophageal varices (EV), a serious health concern resulting from portal hypertension, are traditionally diagnosed through invasive endoscopic procedures.
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning
The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability of liver tumors.
Attribution Methods in Asset Pricing: Do They Account for Risk?
Consequently, when applying machine learning models, we must ensure that the attribution methods reflect the underlying risks accurately.
DMTG: One-Shot Differentiable Multi-Task Grouping
Given N tasks, we propose to simultaneously identify the best task groups from 2^N candidates and train the model weights simultaneously in one-shot, with the high-order task-affinity fully exploited.
OneRestore: A Universal Restoration Framework for Composite Degradation
In real-world scenarios, image impairments often manifest as composite degradations, presenting a complex interplay of elements such as low light, haze, rain, and snow.
Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
We propose a generative model that compresses discrete representations of each sequence to estimate the Gaussian distribution of vector-quantized common (VQC) latent space between multiple sequences.
Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference
Motivated by this, we propose a novel 3D generalizable relative pose estimation method by elaborating (i) with a 2. 5D shape from an RGB-D reference, (ii) with an off-the-shelf differentiable renderer, and (iii) with semantic cues from a pretrained model like DINOv2.
Optimization-based Structural Pruning for Large Language Models without Back-Propagation
Compared to the moderate size of neural network models, structural weight pruning on the Large-Language Models (LLMs) imposes a novel challenge on the efficiency of the pruning algorithms, due to the heavy computation/memory demands of the LLMs.
VideoTetris: Towards Compositional Text-to-Video Generation
Diffusion models have demonstrated great success in text-to-video (T2V) generation.
Near Optimal Decentralized Optimization with Compression and Momentum Tracking
Communication efficiency has garnered significant attention as it is considered the main bottleneck for large-scale decentralized Machine Learning applications in distributed and federated settings.
ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models
To enhance the capabilities of ConvLLaVA, we propose two critical optimizations.
Ranked #35 on
Visual Question Answering
on MM-Vet
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task inference cost of the primary task.
Dual Relation Mining Network for Zero-Shot Learning
Specifically, we introduce a Dual Attention Block (DAB) for visual-semantic relationship mining, which enriches visual information by multi-level feature fusion and conducts spatial attention for visual to semantic embedding.
Anchor-based Robust Finetuning of Vision-Language Models
Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pretraining data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics.
Convergence of Continuous Normalizing Flows for Learning Probability Distributions
We establish non-asymptotic error bounds for the distribution estimator based on CNFs, in terms of the Wasserstein-2 distance.
Instance-Adaptive and Geometric-Aware Keypoint Learning for Category-Level 6D Object Pose Estimation
(2) The second design is a Geometric-Aware Feature Aggregation module, which can efficiently integrate the local and global geometric information into keypoint features.
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering
To alleviate this issue, we propose the Discriminate->Re-Compose->Re- Solve->Re-Decompose (Dr3) mechanism.
MEDBind: Unifying Language and Multimodal Medical Data Embeddings
Medical vision-language pretraining models (VLPM) have achieved remarkable progress in fusing chest X-rays (CXR) with clinical texts, introducing image-text data binding approaches that enable zero-shot learning and downstream clinical tasks.
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.
Enhancing Vision-Language Pre-training with Rich Supervisions
We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering.
Non-Convex Stochastic Composite Optimization with Polyak Momentum
In this paper, we focus on the stochastic proximal gradient method with Polyak momentum.
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images.
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL).
AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration
Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations.
DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
Our research demonstrates the efficacy of leveraging AIGS and the DiffsFormer architecture to mitigate data scarcity in stock forecasting tasks.
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Graph representation learning on vast datasets, like web data, has made significant strides.
Weaver: Foundation Models for Creative Writing
Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models.
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes.
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
MvKSR: Multi-view Knowledge-guided Scene Recovery for Hazy and Rainy Degradation
High-quality imaging is crucial for ensuring safety supervision and intelligent deployment in fields like transportation and industry.
DeMatch: Deep Decomposition of Motion Field for Two-View Correspondence Learning
Specifically we first decompose the rough motion field that is contaminated by false matches into several different sub-fields which are highly smooth and contain the main energy of the original field.
SD2Event:Self-supervised Learning of Dynamic Detectors and Contextual Descriptors for Event Cameras
To address these challenges a novel event-based keypoint detection method is proposed by learning dynamic detectors and contextual descriptors in a self-supervised manner (SD2Event) including a contextual feature descriptor learning (CFDL) module and a dynamic keypoint detector learning (DKDL) module.
I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
I2V-Adapter adeptly propagates the unnoised input image to subsequent noised frames through a cross-frame attention mechanism, maintaining the identity of the input image without any changes to the pretrained T2V model.
Inferring Hybrid Neural Fluid Fields from Videos
Specifically, to deal with visual ambiguities of fluid velocity, we introduce a set of physics-based losses that enforce inferring a physically plausible velocity field, which is divergence-free and drives the transport of density.
Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task.
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection
The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies.
Gaussian Interpolation Flows
Gaussian denoising has emerged as a powerful method for constructing simulation-free continuous normalizing flows for generative modeling.
H2 suboptimal containment control of homogeneous and heterogeneous multi-agent systems
The aim is to design a distributed protocol by dynamic output feedback that achieves state/output containment control while the associated H2 cost is smaller than an a priori given upper bound.
EControl: Fast Distributed Optimization with Compression and Error Control
Error Compensation (EC) is an extremely popular mechanism to mitigate the aforementioned issues during the training of models enhanced by contractive compression operators.
E3 TTS: Easy End-to-End Diffusion-based Text to Speech
Instead, E3 TTS models the temporal structure of the waveform through the diffusion process.
Generalized Minimum Error with Fiducial Points Criterion for Robust Learning
In this paper, a generalized minimum error with fiducial points criterion (GMEEF) is presented by adopting the Generalized Gaussian Density (GGD) function as kernel.
A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images
Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients.
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network
Liver tumor segmentation and classification are important tasks in computer aided diagnosis.
On the application of Large Language Models for language teaching and assessment technology
The recent release of very large language models such as PaLM and GPT-4 has made an unprecedented impact in the popular media and public consciousness, giving rise to a mixture of excitement and fear as to their capabilities and potential uses, and shining a light on natural language processing research which had not previously received so much attention.
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?'
Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism
DWIs with different b-values are fused to efficiently utilize the difference features of DWIs.
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons.
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing
The presence of non-homogeneous haze can cause scene blurring, color distortion, low contrast, and other degradations that obscure texture details.
How to Design Translation Prompts for ChatGPT: An Empirical Study
Machine translation relies heavily on the abilities of language understanding and generation.
Modeling of Interface Loads for EOD Suit Wearers
Explosive Ordnance Disposal (EOD) suits are widely used to protect human operators to execute emergency tasks such as bomb disposal and neutralization.
Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques
As a result, there is a need to develop a productive prediction model for better order execution and adaptability to different datasets.
IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data
Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information.
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences.
D2Former: Jointly Learning Hierarchical Detectors and Contextual Descriptors via Agent-Based Transformers
Second, the HKDL module can generate keypoint detectors in a hierarchical way, which is helpful for detecting keypoints with diverse levels of structures.
Wormhole MAML: Meta-Learning in Glued Parameter Space
In this paper, we introduce a novel variation of model-agnostic meta-learning, where an extra multiplicative parameter is introduced in the inner-loop adaptation.
1st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation
The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer.
MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection
In addition, our MFFN exploits the dependence and interaction between views and channels.
Statistical Inference for Fisher Market Equilibrium
In resource allocation, it is crucial to quantify the variability of the resource received by the agents (such as blood banks and food banks) in addition to fairness and efficiency properties of the systems.
Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer
In this paper, we introduce the ground-to-aerial perception knowledge transfer and propose a progressive semi-supervised learning framework that enables drone perception using only labeled data of ground viewpoint and unlabeled data of flying viewpoints.
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation
Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels.
Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion
Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e. g. difficulty in guaranteeing intra- and inter-operator repeatability.
Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks
In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources.
Composable Text Controls in Latent Space with ODEs
This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
Ranked #2 on
Unsupervised Text Style Transfer
on Yelp
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap
As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation.
VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation
The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.
Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering
With a multiscale sampling, RMI iterates the interaction of appearance-motion information at each scale and the question embeddings to build the multilevel question-guided visual representations.
Rumor Detection with Self-supervised Learning on Texts and Social Graph
At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
Bidding Agent Design in the LinkedIn Ad Marketplace
We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces.
Finding Dynamics Preserving Adversarial Winning Tickets
Modern deep neural networks (DNNs) are vulnerable to adversarial attacks and adversarial training has been shown to be a promising method for improving the adversarial robustness of DNNs.
Semi-Supervised Video Semantic Segmentation With Inter-Frame Feature Reconstruction
For this task, we observe that the overfitting is surprisingly severe between labeled and unlabeled frames within a training video although they are very similar in style and contents.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
Towards Panoptic 3D Parsing for Single Image in the Wild
Our proposed panoptic 3D parsing framework points to a promising direction in computer vision.
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution.
Abnormal Occupancy Grid Map Recognition using Attention Network
The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it.
FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation
To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task.
Ranked #13 on
Semantic Segmentation
on FMB Dataset
Relative Entropy Gradient Sampler for Unnormalized Distributions
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions.
Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering
Targeting these issues, this paper proposes a novel Temporal Pyramid Transformer (TPT) model with multimodal interaction for VideoQA.
Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth
For such issues, we propose a novel Learning to Agreement (Learn2Agree) framework to tackle the challenge of learning from multiple annotators without objective ground truth.
A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries.
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation
It simply encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation.
1st Place Solutions for UG2+ Challenge 2021 -- (Semi-)supervised Face detection in the low light condition
In this technical report, we briefly introduce the solution of our team "TAL-ai" for (Semi-) supervised Face detection in the low light condition in UG2+ Challenge in CVPR 2021.
Boosting Light-Weight Depth Estimation Via Knowledge Distillation
However, this KD process can be challenging and insufficient due to the large model capacity gap between the teacher and the student.
Multi-Party Dual Learning
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data.
Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework
Since the existing naive model parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogy multi-party learning as a many-to-one knowledge sharing problem.
Principled Ultrasound Data Augmentation for Classification of Standard Planes
In this paper, we present an augmentation policy search method with the goal of improving model classification performance.
Significant Inverse Magnetocaloric Effect induced by Quantum Criticality
Strong fluctuations in the low-$T$ quantum critical regime can give rise to a large thermal entropy change and thus significant cooling effect when approaching the QCP.
Strongly Correlated Electrons
Factor-augmented Smoothing Model for Functional Data
We propose modeling raw functional data as a mixture of a smooth function and a highdimensional factor component.
Methodology Statistics Theory Statistics Theory
Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion
Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i. e., where the highlight moments are in long videos) for supervised learning.
Exploiting Learnable Joint Groups for Hand Pose Estimation
This is different from the previous methods where all the joints are considered holistically and share the same feature.
Generative Learning With Euler Particle Transport
The key task in training is the estimation of the density ratios or differences that determine the residual maps.
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states.
Partial FC: Training 10 Million Identities on a Single Machine
The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks.
Ranked #2 on
Face Identification
on MegaFace
WeChat Neural Machine Translation Systems for WMT20
We participate in the WMT 2020 shared news translation task on Chinese to English.
Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining
As a spontaneous expression of emotion on face, micro-expression reveals the underlying emotion that cannot be controlled by human.
CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling
Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and possibly incomplete, which restricts its use for a high-quality surface recovery.
PP-YOLO: An Effective and Efficient Implementation of Object Detector
We mainly try to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged.
Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement
In the first stage, heatmap regression network is applied to obtain a rough localization result, and a set of proposal keypoints, called guided points, are sampled.
An Improved Analysis of Stochastic Gradient Descent with Momentum
Furthermore, the role of dynamic parameters has not been addressed.
Optimization and Control
Data-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions
To construct an efficient and stable approximation for the Langevin dynamics on $\mathcal{N}$, we leverage the corresponding Fokker-Planck equation on the manifold $\mathcal{N}$ in terms of the reaction coordinates $\mathsf{y}$.
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
An outbreak of a novel coronavirus disease (i. e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world.
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning
We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL).
Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$.
Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency
Specifically, CycleST is composed of an encoder-decoder network and a residual learning strategy that restore the shearlet coefficients of densely-sampled EPIs using EPI reconstruction and cycle consistency losses.
Signal Processing Multimedia Image and Video Processing
DRST: Deep Residual Shearlet Transform for Densely Sampled Light Field Reconstruction
Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully Convolutional Neural Network (CNN) to predict the residuals of the shearlet coefficients in shearlet domain in order to reconstruct a densely-sampled EPI in image domain.
Application of Deep Q-Network in Portfolio Management
This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market.
Learning Implicit Generative Models with Theoretical Guarantees
We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution.
Automated Testing for Deep Learning Systems with Differential Behavior Criteria
By observing differential behaviors from three pre-trained models during each testing iteration, the input image that triggered erroneous feedback was registered as a corner-case.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.
Skew-Explore: Learn faster in continuous spaces with sparse rewards
In this work, we introduce an exploration approach based on maximizing the entropy of the visited states while learning a goal-conditioned policy.
Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction
In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction.
Intra-Ensemble in Neural Networks
In this work, we propose Intra-Ensemble, an end-to-end ensemble strategy with stochastic channel recombination operations to train several sub-networks simultaneously within one neural network.
Increasing Iterate Averaging for Solving Saddle-Point Problems
In particular, the increasing averages consistently outperform the uniform averages in all test problems by orders of magnitude.
Wasserstein-Wasserstein Auto-Encoders
To address the challenges in learning deep generative models (e. g., the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE).
Deep Generative Learning via Variational Gradient Flow
We propose a general framework to learn deep generative models via \textbf{V}ariational \textbf{Gr}adient Fl\textbf{ow} (VGrow) on probability spaces.
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge.
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
This report demonstrates our solution for the Open Images 2018 Challenge.
Unity: A General Platform for Intelligent Agents
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments.
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction
In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks.
Ranked #93 on
Semantic Segmentation
on NYU Depth v2
Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance.
Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art
We use automatic speech recognition to assess spoken English learner pronunciation based on the authentic intelligibility of the learners' spoken responses determined from support vector machine (SVM) classifier or deep learning neural network model predictions of transcription correctness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Convex Geometry of the Generalized Matrix-Fractional Function
Generalized matrix-fractional (GMF) functions are a class of matrix support functions introduced by Burke and Hoheisel as a tool for unifying a range of seemingly divergent matrix optimization problems associated with inverse problems, regularization and learning.
Symmetric Non-Rigid Structure from Motion for Category-Specific Object Structure Estimation
This paper addresses the estimation of 3D structures of symmetric objects from multiple images of the same object category, e. g. different cars, seen from various viewpoints.
Semi-Supervised Sparse Representation Based Classification for Face Recognition with Insufficient Labeled Samples
This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e. g., different lighting conditions, different glasses).
Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images
By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single- or multiple-image from the same category, e. g., multiple different cars.
Deep Gate Recurrent Neural Network
This paper introduces two recurrent neural network structures called Simple Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general structures for learning long term dependencies.
