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Found 145 papers, 47 papers with code
Gestures Are Used Rationally: Information Theoretic Evidence from Neural Sequential Models
Verbal communication is companied by rich non-verbal signals.
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues
We describe a new freely available Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships.
Deep regularization networks for inverse problems with noisy operators
A supervised learning approach is proposed for regularization of large inverse problems where the main operator is built from noisy data.
Think Before You Accept: Semantic Reflective Verification for Faster Speculative Decoding
Using prompt-based probing, we obtain both the original and reflective distributions of draft tokens in a single forward pass.
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query
By using these lookahead queries as the observation window for importance estimation, LAQ achieves more consistent and accurate KV cache eviction aligned with real inference scenarios.
Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) with general parametrization.
Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model
Then, we use these two models to reconstruct HR-HSI from two low-dimensional components, i. e, the spectral basis and the reduced coefficient, during the reverse diffusion process.
Team Networks with Partially Observed Links
This paper studies a linear production model in team networks with missing links.
Pay More Attention to the Robustness of Prompt for Instruction Data Mining
Instruction tuning has emerged as a paramount method for tailoring the behaviors of LLMs.
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data
Specifically, HierFedLoRA partitions all devices into multiple near-IID groups and adjusts the intra-group aggregation frequency for each group to eliminate the negative effects of non-IID data.
A Survey on Transformer Context Extension: Approaches and Evaluation
Large language models (LLMs) based on Transformer have been widely applied in the filed of natural language processing (NLP), demonstrating strong performance, particularly in handling short text tasks.
Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning
We present the first finite-sample analysis for policy evaluation in robust average-reward Markov Decision Processes (MDPs).
A Review of Causal Decision Making
To make effective decisions, it is important to have a thorough understanding of the causal relationships among actions, environments, and outcomes.
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Pursuing invariant prediction from heterogeneous environments opens the door to learning causality in a purely data-driven way and has several applications in causal discovery and robust transfer learning.
Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis
A low-rank strategy is used to reduce the complexity of parameters in learning hypergraph structures, while a Gumbel-Softmax-based sampling strategy optimizes the patch distribution across hyperedges.
Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
We address the problem of quantum reinforcement learning (QRL) under model-free settings with quantum oracle access to the Markov Decision Process (MDP).
Lightweight and Post-Training Structured Pruning for On-Device Large Lanaguage Models
Considering the hardware-friendly characteristics and broad applicability, structured pruning has emerged as an efficient solution to reduce the resource demands of large language models (LLMs) on resource-constrained devices.
High-Precision Fabric Defect Detection via Adaptive Shape Convolutions and Large Kernel Spatial Modeling
Detecting fabric defects in the textile industry remains a challenging task due to the diverse and complex nature of defect patterns.
Efficient Deployment of Large Language Models on Resource-constrained Devices
Deploying Large Language Models (LLMs) on resource-constrained (or weak) devices presents significant challenges due to limited resources and heterogeneous data distribution.
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
Herein, based on the inherent characteristics of FedFT, we observe that LoRA layers with higher ranks added close to the output help to save resource consumption while achieving comparable fine-tuning performance.
Tracking the Feature Dynamics in LLM Training: A Mechanistic Study
Understanding training dynamics and feature evolution is crucial for the mechanistic interpretability of large language models (LLMs).
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits
In this paper, we optimize the task by constructing MBTIBench, the first manually annotated high-quality MBTI personality detection dataset with soft labels, under the guidance of psychologists.
Multi-robot autonomous 3D reconstruction using Gaussian splatting with Semantic guidance
To further reduce time cost of task generation and improve reconstruction quality, we integrate online open-vocabulary semantic segmentation with surface uncertainty of 3DGS, focusing view sampling on regions with high instance uncertainty.
TaQ-DiT: Time-aware Quantization for Diffusion Transformers
Transformer-based diffusion models, dubbed Diffusion Transformers (DiTs), have achieved state-of-the-art performance in image and video generation tasks.
Word reuse and combination support efficient communication of emerging concepts
A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization.
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
To address the gap, we introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A Distributed Strategy Generalized to Arbitrary User Distributions
To make the optimization tractable, a multi-agent CNN-enhanced deep Q learning (MA-CDQL) algorithm is proposed.
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring
Existing black-box jailbreak methods often rely on model feedback, repeatedly submitting queries with detectable malicious instructions during the attack search process.
Network scaling and scale-driven loss balancing for intelligent poroelastography
This forms the basis for explicit scaling of the loss components and their derivatives with respect to the network parameters.
Enhancing Recommendation with Denoising Auxiliary Task
The historical interaction sequences of users plays a crucial role in training recommender systems that can accurately predict user preferences.
Linear Contextual Bandits with Interference
Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others.
Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning
To overcome these issues, in this article, an unsupervised blind fusion method for hyperspectral and multispectral images based on Tucker decomposition and spatial spectral manifold learning (DTDNML) is proposed.
Distributed Clustering based on Distributional Kernel
This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final clusters based on the similarity with respect to the distributions of initial clusters, as measured by K. It is the only framework that satisfies all three of the following properties.
Layerwise Change of Knowledge in Neural Networks
This paper aims to explain how a deep neural network (DNN) gradually extracts new knowledge and forgets noisy features through layers in forward propagation.
NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid Models
To overcome these limitations, we propose NASH, a Neural architecture and Accelerator Search framework for multiplication-reduced Hybrid models.
Multi-Task Combinatorial Bandits for Budget Allocation
Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year.
SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning
Federated learning, an innovative network architecture designed to safeguard user privacy, is gaining widespread adoption in the realm of technology.
Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training
The emerging In-network Aggregation (INA) has been proposed to integrate with PS to mitigate its incast issue.
Towards the Dynamics of a DNN Learning Symbolic Interactions
This study proves the two-phase dynamics of a deep neural network (DNN) learning interactions.
Concise and Precise Context Compression for Tool-Using Language Models
However, when compressing tool documentation, existing methods suffer from the weaknesses of key information loss (specifically, tool/parameter name errors) and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Evaluation of Bias Towards Medical Professionals in Large Language Models
GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases when evaluating medical professionals for residency selection.
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood
Human and model-generated texts can be distinguished by examining the magnitude of likelihood in language.
Paraphrase Types Elicit Prompt Engineering Capabilities
In particular, changes in morphology and lexicon, i. e., the vocabulary used, showed promise in improving prompts.
Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement
Decompilation transforms compiled code back into a high-level programming language for analysis when source code is unavailable.
RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
In RefChecker, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference.
Evaluation of Machine Translation Based on Semantic Dependencies and Keywords
To achieve a comprehensive and in-depth evaluation of the semantic correctness of sentences, the experimental results show that the accuracy of the evaluation algorithm has been improved compared with similar methods, and it can more accurately measure the semantic correctness of machine translation.
Enhancing Sign Language Teaching: A Mixed Reality Approach for Immersive Learning and Multi-Dimensional Feedback
Furthermore, we use mixed reality technology to construct a scenario-based 3D sign language classroom and explore the user experience of scenario teaching.
Learning to Score Sign Language with Two-stage Method
Human action recognition and performance assessment have been hot research topics in recent years.
Improving Language Model Reasoning with Self-motivated Learning
The framework motivates the model itself to automatically generate rationales on existing datasets.
Toward Informal Language Processing: Knowledge of Slang in Large Language Models
Our work offers a comprehensive evaluation and a high-quality benchmark on English slang based on the OpenSubtitles corpus, serving both as a publicly accessible resource and a platform for applying tools for informal language processing.
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities
With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we introduce the Gemini 1. 5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #1 on
Zero-Shot Video Question Answer
on Zero-shot Video Question Answering on LongVideoBench
(using extra training data)
The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing
While existing works consider specific algorithms to realize invariance learning, we show that model has the potential to learn invariance through standard training procedures.
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection
This paper presents a robust watermark embedding scheme, named RobWE, to protect the ownership of personalized models in PFL.
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
In this paper, we provide the first systematic review on the applications of deep generative models for offline policy learning.
MULTI: Multimodal Understanding Leaderboard with Text and Images
The rapid development of multimodal large language models (MLLMs) raises the question of how they compare to human performance.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Visual Question Answering (VQA)
on InfographicVQA
Rethinking the Instruction Quality: LIFT is What You Need
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data.
Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization
In this paper, we open up a new way to design and analyze gradient-based algorithms with direct applications in machine learning, including linear regression and beyond.
Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties
To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper.
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems.
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
Through thorough theoretical analysis, we demonstrate that the quantum advantage in mean estimation leads to exponential advancements in regret guarantees for infinite horizon Reinforcement Learning.
RLLTE: Long-Term Evolution Project of Reinforcement Learning
We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application.
CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization
We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i. e., GP-based BO (GPBO).
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data at the network edge.
OSP: Boosting Distributed Model Training with 2-stage Synchronization
However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively.
Collaborative Optimization of Multi-microgrids System with Shared Energy Storage Based on Multi-agent Stochastic Game and Reinforcement Learning
The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746. 5kW in 24 hours and achieve a cost reduction of 16. 21% in the test.
Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)
In addition, the generalization ability of the Lie group-CNN on SIM(2) on rotation-equivariance is verified on rotated-MNIST and rotated-CIFAR10, and the robustness of the network is verified on SO(2) and SE(2).
Dataset Management Platform for Machine Learning
Effective dataset management, including tasks such as data cleanup, versioning, access control, dataset transformation, automation, integrity and security, etc., can help improve the efficiency and speed of the machine learning process.
Short-term and long-term memory self-attention network for segmentation of tumours in 3D medical images
Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes.
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy
Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images.
Deep learning for full-field ultrasonic characterization
This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform for distributed reconstruction of mechanical properties in layered components from full waveform data.
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy.
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning
Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy.
Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning
Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data.
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.
Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS-Tagging and Enhanced Semantic-Awareness
Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process.
ReAFFPN: Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection
This paper proposes a Rotation-equivariant Attention Feature Fusion Pyramid Networks for Aerial Object Detection named ReAFFPN.
Tracing Semantic Variation in Slang
However, slang semantic variation is not well understood and under-explored in the natural language processing of slang.
Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming
The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does.
KE-QI: A Knowledge Enhanced Article Quality Identification Dataset
With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media.
Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality
Using data from 80 languages in 17 language families and phylogenetic modeling, we demonstrate that languages evolve to balance these pressures, such that word order change is accompanied by change in the frequency distribution of the syntactic structures which speakers communicate to maintain overall efficiency.
Language Anisotropic Cross-Lingual Model Editing
On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing.
Noun2Verb: Probabilistic frame semantics for word class conversion
We present a formal framework, Noun2Verb, that simulates the production and comprehension of novel denominal verb usages by modeling shared knowledge of speaker and listener in semantic frames.
Semantically Informed Slang Interpretation
Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret.
Image Captioning In the Transformer Age
This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Combinatorial optimization is a well-established area in operations research and computer science.
Neural reality of argument structure constructions
Second, in a "Jabberwocky" priming-based experiment, we find that LMs associate ASCs with meaning, even in semantically nonsensical sentences.
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction.
HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic Analysis
Contextualized word embeddings have demonstrated state-of-the-art performance in various natural language processing tasks including those that concern historical semantic change.
ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation
The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models.
Nonlinear Intensity Underwater Sonar Image Matching Method Based on Phase Information and Deep Convolution Features
In the field of deep-sea exploration, sonar is presently the only efficient long-distance sensing device.
Breaking the curse of dimensionality with Isolation Kernel
The curse of dimensionality has been studied in different aspects.
Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining
Natural language relies on a finite lexicon to express an unbounded set of emerging ideas.
An unsupervised framework for tracing textual sources of moral change
Morality plays an important role in social well-being, but people's moral perception is not stable and changes over time.
Evolution of emotion semantics
This consensus may give the impression that their meaning is static, but we propose this is not the case.
What do writing features tell us about AI papers?
As the numbers of submissions to conferences grow quickly, the task of assessing the quality of academic papers automatically, convincingly, and with high accuracy attracts increasing attention.
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices.
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems
Recently deep neural networks (DNNs) have achieved tremendous success for object detection in overhead (e. g., satellite) imagery.
A diachronic evaluation of gender asymmetry in euphemism
We assembled a list of 106 euphemism-taboo pairs to analyze their relative use through time by each gender in the corpora.
How is BERT surprised? Layerwise detection of linguistic anomalies
Transformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly.
Integration of Unpaired Single-cell Chromatin Accessibility and Gene Expression Data via Adversarial Learning
Deep learning has empowered analysis for single-cell sequencing data in many ways and has generated deep understanding about a range of complex cellular systems.
A Computational Framework for Slang Generation
Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems.
Self Sparse Generative Adversarial Networks
In this work, we propose a Self Sparse Generative Adversarial Network (Self-Sparse GAN) that reduces the parameter space and alleviates the zero gradient problem.
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Ranked #1 on
Key Information Extraction
on SROIE
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
We tackle implicit discourse relation recognition.
Semantic coordinates analysis reveals language changes in the AI field
Semantic shifts can reflect changes in beliefs across hundreds of years, but it is less clear whether trends in fast-changing communities across a short time can be detected.
A robust low data solution: dimension prediction of semiconductor nanorods
Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution.
Inferring symmetry in natural language
Our results show that a hybrid transfer learning model that integrates linguistic features with contextualized language models most faithfully predicts the empirical data.
Word class flexibility: A deep contextualized approach
Word class flexibility refers to the phenomenon whereby a single word form is used across different grammatical categories.
Contextualized moral inference
Our methodology builds on recent work in contextualized language models and textual inference of moral sentiment.
Improving probability selecting based weights for Satisfiability Problem
SelectNTS is an improved probability selecting based local search algorithm for SAT problem.
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.
A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems
Moreover, its gradient complexity to obtain an $\varepsilon$-stationary point of the objective function is bounded by $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp., $\mathcal{O}\left( \varepsilon ^{-4} \right)$) under the strongly convex-nonconcave (resp., convex-nonconcave) setting.
The Typology of Polysemy: A Multilingual Distributional Framework
Lexical semantic typology has identified important cross-linguistic generalizations about the variation and commonalities in polysemy patterns---how languages package up meanings into words.
Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millennia
Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE.
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations.
A Computational Investigation on Denominalization
Language has been a dynamic system and word meanings always have been changed over times.
Application of Pre-training Models in Named Entity Recognition
Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task.
Text-based inference of moral sentiment change
We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora.
Slang Detection and Identification
The prevalence of informal language such as slang presents challenges for natural language systems, particularly in the automatic discovery of flexible word usages.
HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding
This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing.
Ranked #1 on
UCCA Parsing
on CoNLL 2019
Treat the Word As a Whole or Look Inside? Subword Embeddings Model Language Change and Typology
We use a variant of word embedding model that incorporates subword information to characterize the degree of compositionality in lexical semantics.
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB).
Automated Essay Scoring based on Two-Stage Learning
Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e. g. the essays composed of permuted sentences and the prompt-irrelevant essays.
C2TCP: A Flexible Cellular TCP to Meet Stringent Delay Requirements
Since current widely available network protocols/systems are mainly throughput-oriented designs, meeting stringent delay requirements of new applications such as virtual reality and vehicle-to-vehicle communications on cellular network requires new network protocol/system designs.
Networking and Internet Architecture
Is Nike female? Exploring the role of sound symbolism in predicting brand name gender
Are brand names such as Nike female or male?
Using active learning to expand training data for implicit discourse relation recognition
We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans.
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events.
Cellular Controlled Delay TCP (C2TCP)
To cope with these challenges, we present C2TCP, a flexible end-to-end solution targeting interactive applications requiring high throughput and low delay in cellular networks.
Networking and Internet Architecture
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings
Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models.
Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power
Linguistic alignment between dialogue partners has been claimed to be affected by their relative social power.
Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance
We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication.
Redefinition of the concept of fuzzy set based on vague partition from the perspective of axiomatization
Based on the in-depth analysis of the essence and features of vague phenomena, this paper focuses on establishing the axiomatical foundation of membership degree theory for vague phenomena, presents an axiomatic system to govern membership degrees and their interconnections.
Implicitly Incorporating Morphological Information into Word Embedding
In this paper, we propose three novel models to enhance word embedding by implicitly using morphological information.
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks.
Inference and communication in the game of Password
Second, we predict that speakers and hearers are calibrated, and that both make accurate assumptions about the strategy used by the other.
