Search Results for author:
Found 123 papers, 32 papers with code
Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching
Asymmetric text matching has becoming increasingly indispensable for many downstream tasks (e. g., IR and NLP).
Deep Reinforcement Learning with Smooth Policy
In contrast to policy parameterized by linear/reproducing kernel functions, where simple regularization techniques suffice to control smoothness, for neural network based reinforcement learning algorithms, there is no readily available solution to learn a smooth policy.
A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network
The high accuracy in detecting and categorization blood cancer detection using CNN suggests that the CNN model is promising in blood cancer disease detection.
Improving the Precision of CNNs for Magnetic Resonance Spectral Modeling
Magnetic resonance spectroscopic imaging is a widely available imaging modality that can non-invasively provide a metabolic profile of the tissue of interest, yet is challenging to integrate clinically.
A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning
This study proposed a novel technique that combined thermal stereography and deep learning to achieve fully noncontact wave measurements.
MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU
This paper introduces a novel approach, MIDAS, leveraging a multi-level intent, domain, and slot knowledge distillation for multi-turn NLU.
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval
While Automatic Speech Recognition (ASR) text derived from the short or live-stream videos is readily accessible, how to de-noise the excessively noisy text for multimodal representation learning is mostly untouched.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A reference frame-based microgrid primary control for ensuring global convergence to a periodic orbit
Without the simplifying assumption, however, the steady state being studied is basically a limit cycle with the convergence of its orbit in question.
Spatiotemporal Graph Guided Multi-modal Network for Livestreaming Product Retrieval
The LPR task encompasses three primary dilemmas in real-world scenarios: 1) the recognition of intended products from distractor products present in the background; 2) the video-image heterogeneity that the appearance of products showcased in live streams often deviates substantially from standardized product images in stores; 3) there are numerous confusing products with subtle visual nuances in the shop.
Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base
This paper introduces Golden-Retriever, designed to efficiently navigate vast industrial knowledge bases, overcoming challenges in traditional LLM fine-tuning and RAG frameworks with domain-specific jargon and context interpretation.
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most diverse publicly available cardiac k-space dataset.
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation
We test state-of-the-art foundation models for medical image segmentation, including the original SAM, medical SAM and SAT models, to evaluate segmentation efficacy across different demographic groups and identify disparities.
Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning
Based on an empirical study on the knowledge intensity of the kernel elements of the neural network, we find that the center kernel is the key for maximizing the knowledge intensity for learning new data, while freezing the other kernel elements would get a good balance on the model's capacity for overcoming catastrophic forgetting.
Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing
In this work, we propose a novel FD model by integrating multi-scale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiGRU), and cross self-attention feature fusion (CSAFF).
Learning Multi-dimensional Human Preference for Text-to-Image Generation
Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans.
Learning Coarse-Grained Dynamics on Graph
We consider a Graph Neural Network (GNN) non-Markovian modeling framework to identify coarse-grained dynamical systems on graphs.
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival
Integrating the different data modalities of cancer patients can significantly improve the predictive performance of patient survival.
Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items.
Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach
In modern decentralized applications, ensuring communication efficiency and privacy for the users are the key challenges.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
A Novel Loss Function-based Support Vector Machine for Binary Classification
The previous support vector machine(SVM) including $0/1$ loss SVM, hinge loss SVM, ramp loss SVM, truncated pinball loss SVM, and others, overlooked the degree of penalty for the correctly classified samples within the margin.
A Unified and General Framework for Continual Learning
Extensive experiments on CL benchmarks and theoretical analysis demonstrate the effectiveness of the proposed refresh learning.
Knowledge Condensation and Reasoning for Knowledge-based VQA
We condense the retrieved knowledge passages from two perspectives.
SELECTOR: Heterogeneous graph network with convolutional masked autoencoder for multimodal robust prediction of cancer survival
To mitigate the impact of missing features within the modality on prediction accuracy, we devised a convolutional masked autoencoder (CMAE) to process the heterogeneous graph post-feature reconstruction.
DragAnything: Motion Control for Anything using Entity Representation
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation.
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure
It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure.
Divide-Conquer-and-Merge: Memory- and Time-Efficient Holographic Displays
Both simulations and experiments were conducted to demonstrate the capabilities of the proposed framework.
Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition
Though the strong capabilities of learning the non-linearity of the load patterns and the high prediction accuracy have been achieved, the interpretability of typical deep learning models for electricity load forecasting is less studied.
A Flying Bird Object Detection Method for Surveillance Video
Aiming at the specific characteristics of flying bird objects in surveillance video, such as the typically non-obvious features in single-frame images, small size in most instances, and asymmetric shapes, this paper proposes a Flying Bird Object Detection method for Surveillance Video (FBOD-SV).
DisControlFace: Adding Disentangled Control to Diffusion Autoencoder for One-shot Explicit Facial Image Editing
We identify the key challenge as the exploration of disentangled conditional control between high-level semantics and explicit parameters (e. g., 3DMM) in the generation process, and accordingly propose a novel diffusion-based editing framework, named DisControlFace.
Paragraph-to-Image Generation with Information-Enriched Diffusion Model
In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation.
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
The performance of OVD greatly relies on the quality of class-agnostic region proposals and pseudo-labels for novel object categories.
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning.
Ranked #91 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition
Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters.
Cross-Domain Product Representation Learning for Rich-Content E-Commerce
It is the first dataset to cover product pages, short videos, and live streams simultaneously, providing the basis for establishing a unified product representation across different media domains.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products.
First-order Policy Optimization for Robust Policy Evaluation
We adopt a policy optimization viewpoint towards policy evaluation for robust Markov decision process with $\mathrm{s}$-rectangular ambiguity sets.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
Anchor Free remote sensing detector based on solving discrete polar coordinate equation
Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box.
Sequential three-way decisions with a single hidden layer feedforward neural network
The experimental results verify that STWD-SFNN has a more compact network on structured datasets than other SFNN models, and has better generalization performance than the competitive models.
Policy Mirror Descent Inherently Explores Action Space
SPMD with the second evaluation operator, namely truncated on-policy Monte Carlo (TOMC), attains an $\tilde{\mathcal{O}}(\mathcal{H}_{\mathcal{D}}/\epsilon^2)$ sample complexity, where $\mathcal{H}_{\mathcal{D}}$ mildly depends on the effective horizon and the size of the action space with properly chosen Bregman divergence (e. g., Tsallis divergence).
Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization
Text summarization has been a crucial problem in natural language processing (NLP) for several decades.
Sentiment analysis and opinion mining on educational data: A survey
In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically.
A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis
Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation
Inspired by this insight, we present Chaos to Order (CtO), a novel approach for SFDA that strives to constrain semantic credibility and propagate label information among target subpopulations.
Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
The eco-toll estimation problem quantifies the expected environmental cost (e. g., energy consumption, exhaust emissions) for a vehicle to travel along a path.
Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE.
Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution
Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning.
First-order Policy Optimization for Robust Markov Decision Process
We consider the problem of solving robust Markov decision process (MDP), which involves a set of discounted, finite state, finite action space MDPs with uncertain transition kernels.
Deep Forest with Hashing Screening and Window Screening
To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening.
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications.
Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids
Modularized Koopman Bilinear Form (M-KBF) is presented to model and predict the transient dynamics of microgrids in the presence of disturbances.
PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement
A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in the presence of disturbances.
Benchmarking Domain Generalization on EEG-based Emotion Recognition
Specifically, numerous domain adaptation (DA) algorithms have been exploited in the past five years to enhance the generalization of emotion recognition models across subjects.
Demand-driven train timetabling for air and intercity high-speed rail synchronization service
We also extend the train timetabling model by considering the synchronization events of flight-train and the train circulation plan.
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios.
DSRRTracker: Dynamic Search Region Refinement for Attention-based Siamese Multi-Object Tracking
Many multi-object tracking (MOT) methods follow the framework of "tracking by detection", which associates the target objects-of-interest based on the detection results.
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
We investigate the role of noise in optimization algorithms for learning over-parameterized models.
Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity
We first establish the global linear convergence of HPMD instantiated with Kullback-Leibler divergence, for both the optimality gap, and a weighted distance to the set of optimal policies.
Block Policy Mirror Descent
Despite the nonconvex nature of the problem and a partial update rule, we provide a unified analysis for several sampling schemes, and show that BPMD achieves fast linear convergence to the global optimality.
OPP-Miner: Order-preserving sequential pattern mining
To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values.
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network
This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures.
DBC-Forest: Deep forest with binning confidence screening
As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications.
SAMCNet for Spatial-configuration-based Classification: A Summary of Results
This problem is challenging due to an exponential number of category subsets which may vary in the strength of spatial interactions.
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL
Theoretically, under a weak coverage assumption that the experience dataset contains enough information about the optimal policy, we prove that for an episodic mean-field MDP with a horizon $H$ and $N$ training trajectories, SAFARI attains a sub-optimality gap of $\mathcal{O}(H^2d_{\rm eff} /\sqrt{N})$, where $d_{\rm eff}$ is the effective dimension of the function class for parameterizing the value function, but independent on the number of agents.
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits
We show that incorporating frequency information of tokens in the embedding learning problems leads to provably efficient algorithms, and demonstrate that common adaptive algorithms implicitly exploit the frequency information to a large extent.
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.
Single Image Dehazing with An Independent Detail-Recovery Network
In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.
MutualGraphNet: A novel model for motor imagery classification
Experiments are conducted on motor imagery EEG data set and we compare our model with the current state-of-the-art approaches and the results suggest that MutualGraphNet is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data
For any BPPA instantiated with a fixed Bregman divergence, we provide a lower bound of the margin obtained by BPPA with respect to an arbitrarily chosen norm.
Contrastive Learning for Cold-Start Recommendation
It aims to maximize the mutual dependencies between item content and collaborative signals.
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.
Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results
Given an on-board diagnostics (OBD) dataset and a physics-based emissions prediction model, this paper aims to develop an accurate and computational-efficient AI (Artificial Intelligence) method that predicts vehicle emissions.
Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
Mapping of spatial hotspots, i. e., regions with significantly higher rates of generating cases of certain events (e. g., disease or crime cases), is an important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc.
Equivariant $\mathbb R$-test configurations and semistable limits of $\mathbb Q$-Fano group compactifications
Let $G$ be a connected, complex reductive group.
Differential Geometry
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems.
IFoodCloud: A Platform for Real-time Sentiment Analysis of Public Opinion about Food Safety in China
The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues.
Estimates of the early EM emission from compact binary mergers
We estimate their luminosities and time scales as functions of the chirp mass which is the most readily constrained parameter from the gravitational wave detections of these events.
High Energy Astrophysical Phenomena
SpotTune: Leveraging Transient Resources for Cost-efficient Hyper-parameter Tuning in the Public Cloud
Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms.
MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation
In recent years, a large number of variants of U-Net based on Multi-scale feature fusion are proposed to improve the segmentation performance for medical image segmentation.
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers
We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits.
A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles
A physics model-guided online Bayesian framework is described and validated on large number of in-use driving samples of EREVs used for last-mile package delivery.
Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles
Adversarial examples are firstly investigated in the area of computer vision: by adding some carefully designed ''noise'' to the original input image, the perturbed image that cannot be distinguished from the original one by human, can fool a well-trained classifier easily.
How to Retrain Recommender System? A Sequential Meta-Learning Method
Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that for any fixed iteration $T$, when the adversarial perturbation during training has proper bounded L2 norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum L2 norm margin classifier at the rate of $O(1/\sqrt{T})$, significantly faster than the rate $O(1/\log T}$ of training with clean data.
TEA: Temporal Excitation and Aggregation for Action Recognition
Temporal modeling is key for action recognition in videos.
Deep Reinforcement Learning with Robust and Smooth Policy
Deep reinforcement learning (RL) has achieved great empirical successes in various domains.
Pursuing Sources of Heterogeneity in Modeling Clustered Population
We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously.
Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head
To the best of our knowledge, this study is the first research on the prospective use of a deep learning-based diagnosis system for AVNFH by conducting two pilot studies representing real-world application scenarios.
Bilinear Graph Neural Network with Neighbor Interactions
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Ranked #4 on
Collaborative Filtering
on Amazon-Book
Towards Understanding the Importance of Noise in Training Neural Networks
Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks.
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data.
Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction
Reliable and accurate prediction of time series plays a crucial role in maritime industry, such as economic investment, transportation planning, port planning and design, etc.
Strain engineering of epitaxial oxide heterostructures beyond substrate limitations
The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.
Materials Science
The Speechtransformer for Large-scale Mandarin Chinese Speech Recognition
Attention-based sequence-to-sequence architectures have made great progress in the speech recognition task.
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification
Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e. g. completely different identities and backgrounds) and the intra-dataset difference (e. g. camera invariance).
Transductive Zero-Shot Learning with Visual Structure Constraint
Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i. e. alleviate the above domain shift problem).
TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph
In this work, we introduce TechKG, a large scale Chinese knowledge graph that is technology-oriented.
Improving Gated Recurrent Unit Based Acoustic Modeling with Batch Normalization and Enlarged Context
The use of future contextual information is typically shown to be helpful for acoustic modeling.
Object detection and tracking benchmark in industry based on improved correlation filter
Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4. 0 applications.
Communication-Efficient Projection-Free Algorithm for Distributed Optimization
We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral.
Projection-Free Algorithms in Statistical Estimation
Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective.
Gated Recurrent Unit Based Acoustic Modeling with Future Context
The use of future contextual information is typically shown to be helpful for acoustic modeling.
Discriminative Learning of Latent Features for Zero-Shot Recognition
In this work, we retrospect existing methods and demonstrate the necessity to learn discriminative representations for both visual and semantic instances of ZSL.
Mixed Supervised Object Detection with Robust Objectness Transfer
Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD.
A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection
In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn.
Fast Global Convergence via Landscape of Empirical Loss
While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing interests recently, due to many desirable properties such as superior robustness and classification accuracy, compared with their convex counterparts.
Machine Learning for Survival Analysis: A Survey
We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.
Solving Multi-Objective MDP with Lexicographic Preference: An application to stochastic planning with multiple quantile objective
In most common settings of Markov Decision Process (MDP), an agent evaluate a policy based on expectation of (discounted) sum of rewards.
SAGA and Restricted Strong Convexity
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications.
Linear Convergence of SVRG in Statistical Estimation
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems.
Skipping Word: A Character-Sequential Representation based Framework for Question Answering
Recent works using artificial neural networks based on word distributed representation greatly boost the performance of various natural language learning tasks, especially question answering.
M$^2$S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection
Recent works using artificial neural networks based on distributed word representation greatly boost performance on various natural language processing tasks, especially the answer selection problem.
Audio Recording Device Identification Based on Deep Learning
The identification result shows that the method of getting feature vector from the noise of each device and identifying them with deep learning techniques is viable, and well-preformed.
Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction
In this way, the learned binary codes can be applied to not only fine-grained face image retrieval, but also facial attributes prediction, which is the very innovation of this work, just like killing two birds with one stone.
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules.
Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold
Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching.
The Knowledge Gradient Policy Using A Sparse Additive Belief Model
We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power.
Comment on "Clustering by fast search and find of density peaks"
For any data set to be clustered, the most reasonable value of d_c can be objectively calculated from the data set by using our proposed method.
Sparse Additive Model using Symmetric Nonnegative Definite Smoothers
We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers.
