Search Results for author:
Found 210 papers, 57 papers with code
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
Semi-Dense 3D Reconstruction with a Stereo Event Camera
Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision.
Event-based Stereo Visual Odometry
We present a solution to the problem of visual odometry from the data acquired by a stereo event-based camera rig.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
On the Continuity of Rotation Representations in Neural Networks
Thus, widely used representations such as quaternions and Euler angles are discontinuous and difficult for neural networks to learn.
GFTE: Graph-based Financial Table Extraction
Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison.
Motion-Attentive Transition for Zero-Shot Video Object Segmentation
In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation.
Ranked #9 on
Unsupervised Video Object Segmentation
on FBMS test
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation.
Iterative Normalization: Beyond Standardization towards Efficient Whitening
With the support of SND, we provide natural explanations to several phenomena from the perspective of optimization, e. g., why group-wise whitening of DBN generally outperforms full-whitening and why the accuracy of BN degenerates with reduced batch sizes.
Ranked #8 on
Robust Object Detection
on DWD
Structure-informed Language Models Are Protein Designers
This paper demonstrates that language models are strong structure-based protein designers.
Hybrid coarse-fine classification for head pose estimation
In this paper, to do the estimation without facial landmarks, we combine the coarse and fine regression output together for a deep network.
Ranked #3 on
Head Pose Estimation
on AFLW
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Learning latent representations of registered meshes is useful for many 3D tasks.
Can SAM Segment Polyps?
Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks.
Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models.
Ranked #37 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN).
Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
We develop a method to identify independently moving objects acquired with an event-based camera, i. e., to solve the event-based motion segmentation problem.
Learning Visibility for Robust Dense Human Body Estimation
An alternative approach is to estimate dense vertices of a predefined template body in the image space.
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
Large language models (LLMs) are displaying emergent abilities for math reasoning tasks, and there is a growing attention on enhancing the ability of open-source LLMs through supervised fine-tuning (SFT). In this paper, we aim to explore a general data strategy for supervised data to help optimize and expand math reasoning ability. Firstly, we determine the ability boundary of reasoning paths augmentation by identifying these paths' minimal optimal set. Secondly, we validate that different abilities of the model can be cumulatively enhanced by Mix of Minimal Optimal Sets of corresponding types of data, while our models MMOS achieve SOTA performance on series base models under much lower construction costs. Besides, we point out GSM-HARD is not really hard and today's LLMs no longer lack numerical robustness. Also, we provide an Auto Problem Generator for robustness testing and educational applications. Our code and data are publicly available at https://github. com/cyzhh/MMOS.
Ranked #2 on
Math Word Problem Solving
on ASDiv-A
(using extra training data)
Enhancing In-context Learning via Linear Probe Calibration
However, applying ICL in real cases does not scale with the number of samples, and lacks robustness to different prompt templates and demonstration permutations.
Delving into the Estimation Shift of Batch Normalization in a Network
We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones.
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems.
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis
To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis.
An Efficient Multilingual Language Model Compression through Vocabulary Trimming
Multilingual language model (LM) have become a powerful tool in NLP especially for non-English languages.
Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset
Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has.
Boosting Multi-modal Model Performance with Adaptive Gradient Modulation
In addition, we find that the jointly trained model typically has a preferred modality on which the competition is weaker than other modalities.
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples.
Small-floating Target Detection in Sea Clutter via Visual Feature Classifying in the Time-Doppler Spectra
It is challenging to detect small-floating object in the sea clutter for a surface radar.
Improving Entity Linking through Semantic Reinforced Entity Embeddings
We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality.
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble
Although deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples.
Feature Aggregation and Propagation Network for Camouflaged Object Detection
In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection.
DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior
To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP).
An Investigation into the Stochasticity of Batch Whitening
Our work originates from the observation that while various whitening transformations equivalently improve the conditioning, they show significantly different behaviors in discriminative scenarios and training Generative Adversarial Networks (GANs).
Group Whitening: Balancing Learning Efficiency and Representational Capacity
Results show that GW consistently improves the performance of different architectures, with absolute gains of $1. 02\%$ $\sim$ $1. 49\%$ in top-1 accuracy on ImageNet and $1. 82\%$ $\sim$ $3. 21\%$ in bounding box AP on COCO.
Dual Residual Attention Network for Image Denoising
The proposed DRANet includes two different parallel branches, which can capture complementary features to enhance the learning ability of the model.
Ranked #1 on
Image Denoising
on SIDD
(Average PSNR metric)
SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms
SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonconvex optimization.
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings
Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word.
Chinese Named Entity Recognition Augmented with Lexicon Memory
Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates.
Chinese Named Entity Recognition
named-entity-recognition
+4
SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities
Our experiments suggest that for some tasks where different modalities are complementary, the multi-modal models still tend to use the dominant modality alone and ignore the cooperation across modalities.
Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving
Previous works on object detection have achieved high accuracy in closed-set scenarios, but their performance in open-world scenarios is not satisfactory.
Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA
Natural language is ambiguous.
Supervised Encoding for Discrete Representation Learning
Classical supervised classification tasks search for a nonlinear mapping that maps each encoded feature directly to a probability mass over the labels.
Can Text-based Knowledge Graph Completion Benefit From Zero-Shot Large Language Models?
We found that (1) without fine-tuning, LLMs have the capability to further improve the quality of entity text descriptions.
using multiple losses for accurate facial age estimation
Age estimation is an essential challenge in computer vision.
Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations
We further develop the incremental (stochastic) reshaped Wirtinger flow (IRWF) and show that IRWF converges linearly to the true signal.
Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings
Our proposed method can combine source sense embeddings that cover different sets of word senses.
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?
We study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable dataset.
Multimodal Learning for Hateful Memes Detection
Memes are used for spreading ideas through social networks.
Solving Cosine Similarity Underestimation between High Frequency Words by L2 Norm Discounting
This similarity underestimation problem is particularly severe for highly frequent words.
Fast Maximum $k$-Plex Algorithms Parameterized by Small Degeneracy Gaps
We define a novel parameter of the input instance, $g_k(G)$, the gap between the degeneracy bound and the size of the maximum $k$-plex in the given graph, and present an exact algorithm parameterized by this $g_k(G)$, which has a worst-case running time polynomial in the size of the input graph and exponential in $g_k(G)$.
Elastic Neural Networks for Classification
In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients.
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without the sharing of raw data.
Listing Maximal k-Plexes in Large Real-World Graphs
Our first contribution is algorithm ListPlex that lists all maximal $k$-plexes in $O^*(\gamma^D)$ time for each constant $k$, where $\gamma$ is a value related to $k$ but strictly smaller than 2, and $D$ is the degeneracy of the graph that is far less than the vertex number $n$ in real-word graphs.
DDDM: a Brain-Inspired Framework for Robust Classification
The mechanism is well described by the Drift-Diffusion Model (DDM).
Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence
We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set.
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
Cubic regularization (CR) is an optimization method with emerging popularity due to its capability to escape saddle points and converge to second-order stationary solutions for nonconvex optimization.
Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization
With strongly convex regularizers, we further establish the generalization error bounds for nonconvex loss functions under proximal SGD with high-probability guarantee, i. e., exponential concentration in probability.
Conditional Accelerated Lazy Stochastic Gradient Descent
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate $O\left(\frac{1}{\varepsilon^4}\right)$.
Random gradient extrapolation for distributed and stochastic optimization
Furthermore, we demonstrate that for stochastic finite-sum optimization problems, RGEM maintains the optimal ${\cal O}(1/\epsilon)$ complexity (up to a certain logarithmic factor) in terms of the number of stochastic gradient computations, but attains an ${\cal O}(\log(1/\epsilon))$ complexity in terms of communication rounds (each round involves only one agent).
Critical Points of Neural Networks: Analytical Forms and Landscape Properties
We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum.
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks
The past decade has witnessed a successful application of deep learning to solving many challenging problems in machine learning and artificial intelligence.
Combining tabu search and graph reduction to solve the maximum balanced biclique problem
The Maximum Balanced Biclique Problem is a well-known graph model with relevant applications in diverse domains.
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Then, by exploiting the Kurdyka-{\L}ojasiewicz (\KL) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc.
From First-Order Logic to Assertional Logic
Then, we show how to extend it by definitions, which are special kinds of knowledge, i. e., assertions.
Structured Production System (extended abstract)
In this extended abstract, we propose Structured Production Systems (SPS), which extend traditional production systems with well-formed syntactic structures.
Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields
This paper presents a robust and efficient semi-dense visual odometry solution for RGB-D cameras.
Communication-Efficient Algorithms for Decentralized and Stochastic Optimization
Our major contribution is to present a new class of decentralized primal-dual type algorithms, namely the decentralized communication sliding (DCS) methods, which can skip the inter-node communications while agents solve the primal subproblems iteratively through linearizations of their local objective functions.
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
Vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for urban traffic surveillance.
A Set Theoretic Approach for Knowledge Representation: the Representation Part
In this paper, we propose a set theoretic approach for knowledge representation.
DAP3D-Net: Where, What and How Actions Occur in Videos?
Action parsing in videos with complex scenes is an interesting but challenging task in computer vision.
Distributed Machine Learning via Sufficient Factor Broadcasting
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.
An optimal randomized incremental gradient method
We first introduce a deterministic primal-dual gradient (PDG) method that can achieve the optimal black-box iteration complexity for solving these composite optimization problems using a primal-dual termination criterion.
Distributed Machine Learning via Sufficient Factor Broadcasting
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.
A Logical Study of Partial Entailment
We introduce a novel logical notion--partial entailment--to propositional logic.
Majority Rule for Belief Evolution in Social Networks
In this paper, we study how an agent's belief is affected by her neighbors in a social network.
A Note on Inexact Condition for Cubic Regularized Newton's Method
This note considers the inexact cubic-regularized Newton's method (CR), which has been shown in \cite{Cartis2011a} to achieve the same order-level convergence rate to a secondary stationary point as the exact CR \citep{Nesterov2006}.
Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
Cubic-regularized Newton's method (CR) is a popular algorithm that guarantees to produce a second-order stationary solution for solving nonconvex optimization problems.
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning.
Asynchronous decentralized accelerated stochastic gradient descent
In this work, we introduce an asynchronous decentralized accelerated stochastic gradient descent type of method for decentralized stochastic optimization, considering communication and synchronization are the major bottlenecks.
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.
Cubic Regularization with Momentum for Nonconvex Optimization
However, such a successful acceleration technique has not yet been proposed for second-order algorithms in nonconvex optimization. In this paper, we apply the momentum scheme to cubic regularized (CR) Newton's method and explore the potential for acceleration.
MR-GAN: Manifold Regularized Generative Adversarial Networks
Despite the growing interest in generative adversarial networks (GANs), training GANs remains a challenging problem, both from a theoretical and a practical standpoint.
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
Discourse parsing is a challenging task and plays a critical role in discourse analysis.
Analysis of Robust PCA via Local Incoherence
We investigate the robust PCA problem of decomposing an observed matrix into the sum of a low-rank and a sparse error matrices via convex programming Principal Component Pursuit (PCP).
Viewpoint-Aware Attentive Multi-View Inference for Vehicle Re-Identification
Vehicle re-identification (re-ID) has the huge potential to contribute to the intelligent video surveillance.
Learning Latent Space Models with Angular Constraints
The large model capacity of latent space models (LSMs) enables them to achieve great performance on various applications, but meanwhile renders LSMs to be prone to overfitting.
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties
In this paper, we provide a necessary and sufficient characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for linear neural networks.
SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks.
Realistic Dynamic Facial Textures From a Single Image Using GANs
By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.
Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization
In this paper, we develop novel momentum schemes with flexible coefficient settings to accelerate SPIDER for nonconvex and nonsmooth composite optimization, and show that the resulting algorithms achieve the near-optimal gradient oracle complexity for achieving a generalized first-order stationary condition.
AED-Net: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a long time.
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images
Then, based on initially predicted lesion maps for large quantities of image-level annotated data, a lesion attentive disease grading model is designed to improve the severity classification accuracy.
A unified variance-reduced accelerated gradient method for convex optimization
Moreover, Varag is the first accelerated randomized incremental gradient method that benefits from the strong convexity of the data-fidelity term to achieve the optimal linear convergence.
AiFu at SemEval-2019 Task 10: A Symbolic and Sub-symbolic Integrated System for SAT Math Question Answering
AiFu has won the first place in the SemEval-2019 Task 10 - {''}Math Question Answering{''}competition.
Distributed SGD Generalizes Well Under Asynchrony
The performance of fully synchronized distributed systems has faced a bottleneck due to the big data trend, under which asynchronous distributed systems are becoming a major popularity due to their powerful scalability.
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms.
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.
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
In this paper, we propose a novel scheme, which eliminates backtracking line search but still exploits the information along optimization path by adapting the batch size via history stochastic gradients.
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Two types of zeroth-order stochastic algorithms have recently been designed for nonconvex optimization respectively based on the first-order techniques SVRG and SARAH/SPIDER.
A Deep Learning-Based System for PharmaCoNER
The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).
SpiderBoost and Momentum: Faster Variance Reduction Algorithms
SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonconvex optimization.
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images
In this paper, we propose a diabetic retinopathy generative adversarial network (DR-GAN) to synthesize high-resolution fundus images which can be manipulated with arbitrary grading and lesion information.
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private.
Reanalysis of Variance Reduced Temporal Difference Learning
Furthermore, the variance error (for both i. i. d.\ and Markovian sampling) and the bias error (for Markovian sampling) of VRTD are significantly reduced by the batch size of variance reduction in comparison to those of vanilla TD.
TiFL: A Tier-based Federated Learning System
To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity.
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
Our APG-restart is designed to 1) allow for adopting flexible parameter restart schemes that cover many existing ones; 2) have a global sub-linear convergence rate in nonconvex and nonsmooth optimization; and 3) have guaranteed convergence to a critical point and have various types of asymptotic convergence rates depending on the parameterization of local geometry in nonconvex and nonsmooth optimization.
Generative Tweening: Long-term Inbetweening of 3D Human Motions
We introduce a biomechanically constrained generative adversarial network that performs long-term inbetweening of human motions, conditioned on keyframe constraints.
Momentum with Variance Reduction for Nonconvex Composition Optimization
This paper complements the existing literature by developing various momentum schemes with SPIDER-based variance reduction for non-convex composition optimization.
Recognizing Chinese Judicial Named Entity using BiLSTM-CRF
Named entity recognition (NER) plays an essential role in natural language processing systems.
A New One-Point Residual-Feedback Oracle For Black-Box Learning and Control
When optimizing a deterministic Lipschitz function, we show that the query complexity of ZO with the proposed one-point residual feedback matches that of ZO with the existing two-point schemes.
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples
Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples.
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Specifically, minimizer incoherence measures the discrepancy between the global minimizers of a sample loss and those of the total loss and affects the convergence error of SGD with random reshuffle.
Spatio-temporal Attention Model for Tactile Texture Recognition
Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation.
Learning to Generate Diverse Dance Motions with Transformer
We also introduce new evaluation metrics for the quality of synthesized dance motions, and demonstrate that our system can outperform state-of-the-art methods.
A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability
This dataset has 1, 842 images with pixel-level DR-related lesion annotations, and 1, 000 images with image-level labels graded by six board-certified ophthalmologists with intra-rater consistency.
Exploring the Hierarchy in Relation Labels for Scene Graph Generation
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem.
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
We develop FedCluster--a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties.
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications.
Quantum Orders and Spin Liquids in Cs$_2$CuCl$_4$
Motivated by experiments on Cs$_2$CuCl$_4$ samples, we studied and classified the symmetric spin liquids on triangular lattice.
Strongly Correlated Electrons
UNISON: Unpaired Cross-lingual Image Captioning
In this work, we present a novel unpaired cross-lingual method to generate image captions without relying on any caption corpus in the source or the target language.
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback
As a result, our regret bounds are much tighter compared to existing regret bounds for ZO with conventional one-point feedback, which suggests that ZO with residual feedback can better track the optimizer of online optimization problems.
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
In the Markovian setting, our algorithm achieves the state-of-the-art sample complexity $O(\epsilon^{-1} \log {\epsilon}^{-1})$ that is near-optimal.
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
Using this framework, we show that space-filling sample designs, such as blue noise and Poisson disk sampling, which optimize spectral properties, outperform random designs in terms of the generalization gap and characterize this gain in a closed-form.
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
Specifically, we find that the optimization trajectories of successful DNN trainings consistently obey a certain regularity principle that regularizes the model update direction to be aligned with the trajectory direction.
On the Transferability of Adversarial Attacksagainst Neural Text Classifier
Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.
Contrastive Weight Regularization for Large Minibatch SGD
The minibatch stochastic gradient descent method (SGD) is widely applied in deep learning due to its efficiency and scalability that enable training deep networks with a large volume of data.
Mitigating Bias in Federated Learning
As methods to create discrimination-aware models develop, they focus on centralized ML, leaving federated learning (FL) unexplored.
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
This approach makes the use of gradient boosted trees practical in enterprise federated learning.
Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment
The present paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO, as it efficiently tracks all Canny edge features extracted from the images.
Enhancing Balanced Graph Edge Partition with Effective Local Search
The graph edge partition problem, which is to split the edge set into multiple balanced parts to minimize the total number of copied vertices, has been widely studied from the view of optimization and algorithms.
Global existence for semilinear wave equations with scaling invariant damping in 3-D
Global existence for small data Cauchy problem of semilinear wave equations with scaling invariant damping in 3-D is established in this work, assuming that the data are radial and the constant in front of the damping belongs to $[1. 5, 2)$.
Analysis of PDEs
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry
By leveraging this Lyapunov function and the K{\L} geometry that parameterizes the local geometries of general nonconvex functions, we formally establish the variable convergence of proximal-GDA to a critical point $x^*$, i. e., $x_t\to x^*, y_t\to y^*(x^*)$.
Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
A Deep Emulator for Secondary Motion of 3D Characters
Being a local method, our network is independent of the mesh topology and generalizes to arbitrarily shaped 3D character meshes at test time.
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.
Multi-Agent Off-Policy TD Learning: Finite-Time Analysis with Near-Optimal Sample Complexity and Communication Complexity
Under Markovian sampling and linear function approximation, we proved that the finite-time sample complexity of both algorithms for achieving an $\epsilon$-accurate solution is in the order of $\mathcal{O}(\epsilon^{-1}\ln \epsilon^{-1})$, matching the near-optimal sample complexity of centralized TD(0) and TDC.
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control.
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
Federated learning is an emerging data-private distributed learning framework, which, however, is vulnerable to adversarial attacks.
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation
Temporal-difference learning with gradient correction (TDC) is a two time-scale algorithm for policy evaluation in reinforcement learning.
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM).
Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection
The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network.
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning
This global model can be corrupted when Byzantine workers send malicious gradients, which necessitates robust methods for aggregating gradients that mitigate the adverse effects of Byzantine inputs.
Stochastic Scene-Aware Motion Prediction
A long-standing goal in computer vision is to capture, model, and realistically synthesize human behavior.
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis
Actor-critic (AC) algorithms have been widely adopted in decentralized multi-agent systems to learn the optimal joint control policy.
Assisted Learning for Organizations with Limited Imbalanced Data
In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance.
The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
This paper describes the Volctrans' submission to the WMT21 news translation shared task for German->English translation.
Visual-Textual Attentive Semantic Consistency for Medical Report Generation
Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency.
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis
A category-invariant cross-domain transfer (CCT) method is proposed to address this single-to-multiple extension.
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy
Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well its context.
Escaping Saddle Points in Nonconvex Minimax Optimization via Cubic-Regularized Gradient Descent-Ascent
Our result shows that Cubic-GDA achieves an orderwise faster convergence rate than the standard GDA for a wide spectrum of gradient dominant geometry.
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
Two-player zero-sum Markov game is a fundamental problem in reinforcement learning and game theory.
How to Improve Sample Complexity of SGD over Highly Dependent Data?
Specifically, with a $\phi$-mixing model that captures both exponential and polynomial decay of the data dependence over time, we show that SGD with periodic data-subsampling achieves an improved sample complexity over the standard SGD in the full spectrum of the $\phi$-mixing data dependence.
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization
However, GDA has been proved to converge to stationary points for nonconvex minimax optimization, which are suboptimal compared with local minimax points.
On the Transferability of Adversarial Attacks against Neural Text Classifier
Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models.
Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting
In this paper, we present a cascaded two-level multi-model fitting method for identifying independently moving objects (i. e., the motion segmentation problem) with a monocular event camera.
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones.
An Optimization Principle Of Deep Learning?
In particular, we observe that the trainings that apply the training techniques achieve accelerated convergence and obey the principle with a large $\gamma$, which is consistent with the $\mathcal{O}(1/\gamma K)$ convergence rate result under the optimization principle.
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation
We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots.
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Accelerated Proximal Alternating Gradient-Descent-Ascent for Nonconvex Minimax Machine Learning
Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve a nonconvex minimax optimization problem.
Coordinated Frequency Control through Safe Reinforcement Learning
With widespread deployment of renewables, the electric power grids are experiencing increasing dynamics and uncertainties, with its secure operation being threatened.
On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
In this paper, we propose a novel algorithm based on Gradient Extrapolation Method (GEM-UOT) to find an $\varepsilon$-approximate solution to the UOT problem in $O\big( \kappa \log\big(\frac{\tau n}{\varepsilon}\big) \big)$ iterations with $\widetilde{O}(n^2)$ per-iteration cost, where $\kappa$ is the condition number depending on only the two input measures.
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
Extended Load Flexibility of Industrial P2H Plants: A Process Constraint-Aware Scheduling Approach
The operational flexibility of industrial power-to-hydrogen (P2H) plants enables admittance of volatile renewable power and provides auxiliary regulatory services for the power grid.
A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization
In this work, we study a proximal gradient-type algorithm that adopts the approximate implicit differentiation (AID) scheme for nonconvex bi-level optimization with possibly nonconvex and nonsmooth regularizers.
Data Sampling Affects the Complexity of Online SGD over Dependent Data
Moreover, we show that online SGD with mini-batch sampling can further substantially improve the sample complexity over online SGD with periodic data-subsampling over highly dependent data.
Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings
Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word.
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds
3D deep models consuming point clouds have achieved sound application effects in computer vision.
NeMF: Neural Motion Fields for Kinematic Animation
We present an implicit neural representation to learn the spatio-temporal space of kinematic motions.
A Repulsive Force Unit for Garment Collision Handling in Neural Networks
Despite recent success, deep learning-based methods for predicting 3D garment deformation under body motion suffer from interpenetration problems between the garment and the body.
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation
We then generate prompts by filling manually compiled templates using the extracted pivot and anchor terms.
Federated XGBoost on Sample-Wise Non-IID Data
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
Finite-Time Error Bounds for Greedy-GQ
Our techniques in this paper provide a general approach for finite-sample analysis of non-convex two timescale value-based reinforcement learning algorithms.
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts
Although SikuRoberta significantly boosts performance on WSG and POS tasks on ancient Chinese texts, the lack of labeled data still limits the performance of the model.
On the Curious Case of $\ell_2$ norm of Sense Embeddings
We show that the $\ell_2$ norm of a static sense embedding encodes information related to the frequency of that sense in the training corpus used to learn the sense embeddings.
TTS-Guided Training for Accent Conversion Without Parallel Data
Specifically, a text-to-speech (TTS) system is first pretrained with target-accented speech data.
Self-Paced Learning for Open-Set Domain Adaptation
Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples.
Normal-guided Garment UV Prediction for Human Re-texturing
To edit human videos in a physically plausible way, a texture map must take into account not only the garment transformation induced by the body movements and clothes fitting, but also its 3D fine-grained surface geometry.
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
Edge-cloud Collaborative Learning with Federated and Centralized Features
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy.
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
As part of the training, the parties wish to remove unimportant features in the system to improve generalization, efficiency, and explainability.
Accented Text-to-Speech Synthesis with Limited Data
Both objective and subjective evaluation results show that the accented TTS front-end fine-tuned with a small accented phonetic lexicon (5k words) effectively handles the phonetic variation of accents, while the accented TTS acoustic model fine-tuned with a limited amount of accented speech data (approximately 3 minutes) effectively improves the prosodic rendering including pitch and duration.
Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement
By combining the DPRNN module with Convolution Recurrent Network (CRN), the DPCRN obtained a promising performance in speech separation with a limited model size.
Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
Our approach succeeds in performance while significantly saving inference steps.
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
SF-UniDA methods eliminate the need for direct access to source samples when performing adaptation to the target domain.
GRIP: Generating Interaction Poses Using Latent Consistency and Spatial Cues
In contrast, we introduce GRIP, a learning-based method that takes, as input, the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction.
Edge-aware Feature Aggregation Network for Polyp Segmentation
Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation.
A Tutorial on Uniform B-Spline
This document facilitates understanding of core concepts about uniform B-spline and its matrix representation.
Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
To address these issues, we propose a novel Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them.
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments
Speech super-resolution (SSR) aims to predict a high resolution (HR) speech signal from its low resolution (LR) corresponding part.
Can Word Sense Distribution Detect Semantic Changes of Words?
Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora.
Influence of Acceleration and Deceleration Capability on Machine Tool Feed System Performance
With the increasing demand for high speed and high precision machining of machine tools, the problem of which factors of feed system ultimately determine the performance of machine tools is becoming more and more prominent.
A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models
Various types of social biases have been reported with pretrained Masked Language Models (MLMs) in prior work.
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks.
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
This paper provides a comprehensive review of polyp segmentation algorithms.
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation.
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs. It has been demonstrated that malicious actors can covertly exploit these vulnerabilities in LLMs through poisoning attacks aimed at generating undesirable outputs.
Single-shot General Hyper-parameter Optimization for Federated Learning
We address the problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO).
