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Found 85 papers, 17 papers with code
Enabling variable high spatial resolution retrieval from a long pulse BOTDA sensor
We propose a convolutional neural network (CNN) to process the data of conventional Brillouin optical time domain analysis (BOTDA) sensors, which achieves unprecedented performance improvement that allows to directly retrieve higher spatial resolution (SR) from the sensing system that use long pump pulses.
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models
We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements.
Cooperative Learning for Noisy Supervision
Learning with noisy labels has gained the enormous interest in the robust deep learning area.
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence.
Conjugate Energy-Based Models
In this paper, we propose conjugate energy-based models (CEBMs), a new class of energy-based models that define a joint density over data and latent variables.
Nested Variational Inference
We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting.
Boosting Offline Reinforcement Learning with Residual Generative Modeling
While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.
Mathematical Modeling of Chemotaxis Guided Amoeboid Cell Swimming
Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli.
Collaborative Label Correction via Entropy Thresholding
Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels.
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning
This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning.
Minimization of ion micromotion with artificial neural network
Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work, but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation.
Atomic Physics Quantum Physics
Learning Proposals for Probabilistic Programs with Inference Combinators
Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives.
A Convergent Semi-Proximal Alternating Direction Method of Multipliers for Recovering Internet Traffics from Link Measurements
Our model can be divided into a series of subproblems, which only relate to the traffics in a certain individual time interval.
Optimization and Control
App Developer Centric Trusted Execution Environment
ARM TrustZone is the de-facto hardware TEE implementation on mobile devices like smartphones.
Cryptography and Security
Coalition Game Based Full-duplex Popular Content Distribution in mmWave Vehicular Networks
We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks.
Fairness
Information Theory
Networking and Internet Architecture
Information Theory
Adaptive Tree Wasserstein Minimization for Hierarchical Generative Modeling
In this work, we consider OT over tree metrics, which is more general than the sliced Wasserstein and includes the sliced Wasserstein as a special case, and we propose a fast minimization algorithm in $O(n)$ for the optimal Wasserstein-1 transport plan between two distributions in the tree structure.
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation
This paper describes our system in subtask A of SemEval 2020 Shared Task 4.
Learning the Best Pooling Strategy for Visual Semantic Embedding
Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions.
Memory Group Sampling Based Online Action Recognition Using Kinetic Skeleton Features
First, we combine the spatial and temporal skeleton features to depict the actions, which include not only the geometrical features, but also multi-scale motion features, such that both the spatial and temporal information of the action are covered.
Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction
In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment.
A Relaxed Matching Procedure for Unsupervised BLI
Recently unsupervised Bilingual Lexicon Induction (BLI) without any parallel corpus has attracted much research interest.
Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
High-quality and large-scale data are key to success for AI systems.
Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm
The frequency dependency of Brillouin gain temporal envelope is investigated by simulation, and its impact on the recovered results of deconvolution algorithm is thoroughly analyzed.
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors
Recently, two attention methods are proposed via combining with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) network, which can capture the dependencies of sensing signals in both spatial and temporal domains simultaneously.
Learning Based Distributed Tracking
Inspired by the great success of machine learning in the past decade, people have been thinking about the possibility of improving the theoretical results by exploring data distribution.
Data Structures and Algorithms
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations.
Response to LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
Recently the Chinese video sharing platform Bilibili, has popularised a novel captioning system where user comments are displayed as streams of moving subtitles overlaid on the video playback screen and broadcast to all viewers in real-time.
Modeling nanoconfinement effects using active learning
At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions.
Masked Face Recognition Dataset and Application
These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed.
Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN
This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method.
DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.
Stochastic Normalizing Flows
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics.
Structural-Aware Sentence Similarity with Recursive Optimal Transport
Moreover, we further develop Recursive Optimal Similarity (ROTS) for sentences with the valuable semantic insights from the connections between cosine similarity of weighted average of word vectors and optimal transport.
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games.
Deep learning Markov and Koopman models with physical constraints
Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints.
Computational Physics
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling.
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds
We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods.
An encoding framework with brain inner state for natural image identification
The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain.
Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes
However, these methods require fully annotated object bounding boxes for training, which are incredibly hard to scale up due to the high annotation cost.
Learning to Reconstruct and Understand Indoor Scenes from Sparse Views
This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation of indoor scenes.
Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamental natural language understanding task.
A Variational Approach for Learning from Positive and Unlabeled Data
Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.
Unified Visual-Semantic Embeddings: Bridging Vision and Language With Structured Meaning Representations
We propose the Unified Visual-Semantic Embeddings (Unified VSE) for learning a joint space of visual representation and textual semantics.
ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining
In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model.
Adaptive Learning Material Recommendation in Online Language Education
Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials.
Uneven illumination surface defects inspection based on convolutional neural network
Surface defect inspection based on machine vision is often affected by uneven illumination.
Free Component Analysis: Theory, Algorithms & Applications
We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures.
Wasserstein-Fisher-Rao Document Distance
As a fundamental problem of natural language processing, it is important to measure the distance between different documents.
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge.
Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years.
Composing Modeling and Inference Operations with Probabilistic Program Combinators
We assume that models are dynamic, but that model composition is static, in the sense that combinator application takes place prior to evaluating the model on data.
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification
This paper describes our method that competed at WASSA2018 \textit{Implicit Emotion Shared Task}.
NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination
We participate in two subtasks for English tweets: EI-reg and V-reg.
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets.
Deep Generative Markov State Models
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories.
A Multi-Axis Annotation Scheme for Event Temporal Relations
Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition.
Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow.
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples.
Structured Disentangled Representations
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision
Estimating the travel time of a path is of great importance to smart urban mobility.
A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections
Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals.
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
We present MorphNet, an approach to automate the design of neural network structures.
VAMPnets: Deep learning of molecular kinetics
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations.
Mixed Precision Training
Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x.
Network Vector: Distributed Representations of Networks with Global Context
We propose a neural embedding algorithm called Network Vector, which learns distributed representations of nodes and the entire networks simultaneously.
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity
This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task.
Variational approach for learning Markov processes from time series data
This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model.
Soft Label Memorization-Generalization for Natural Language Inference
Often when multiple labels are obtained for a training example it is assumed that there is an element of noise that must be accounted for.
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study
We examine the impact of a test set question's difficulty to determine if there is a relationship between difficulty and performance.
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters.
Spectral learning of dynamic systems from nonequilibrium data
Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems.
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them.
Building an Evaluation Scale using Item Response Theory
Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1).
Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data
Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses.
The Computational Principles of Learning Ability
It has been quite a long time since AI researchers in the field of computer science stop talking about simulating human intelligence or trying to explain how brain works.
What is Learning? A primary discussion about information and Representation
While for AI and machine learning researchers, it is a consensus that we are not anywhere near the core technique which could bring the Terminator, Number 5 or R2D2 into real life, and there is not even a formal definition about what is intelligence, or one of its basic properties: Learning.
Clustering Assisted Fundamental Matrix Estimation
In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied.
Maximum Margin Clustering for State Decomposition of Metastable Systems
When studying a metastable dynamical system, a prime concern is how to decompose the phase space into a set of metastable states.
ILLINOISCLOUDNLP: Text Analytics Services in the Cloud
Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications.