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Found 121 papers, 36 papers with code
Language Model Guided Interpretable Video Action Reasoning
Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework.
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors.
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs.
Primal Methods for Variational Inequality Problems with Functional Constraints
In this work, we propose a simple primal method, termed Constrained Gradient Method (CGM), for addressing functional constrained variational inequality problems, without necessitating any information on the optimal Lagrange multipliers.
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback
Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate.
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities.
Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective
In this paper, we explore a new angle of this issue: overly detailed training data hinders the model's ability to timely terminate generation, leading to continued outputs beyond visual perception limits.
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein.
Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systems
The potential of Machine Learning Control (MLC) in HVAC systems is hindered by its opaque nature and inference mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision-making.
Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies
The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
Accurately modeling the protein fitness landscapes holds great importance for protein engineering.
3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
Learning performance data (e. g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level.
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately
Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions.
Improving Classification Performance With Human Feedback: Label a few, we label the rest
In the realm of artificial intelligence, where a vast majority of data is unstructured, obtaining substantial amounts of labeled data to train supervised machine learning models poses a significant challenge.
The two-way knowledge interaction interface between humans and neural networks
Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process.
Content-Conditioned Generation of Stylized Free hand Sketches
We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set.
Image classification network enhancement methods based on knowledge injection
The current deep neural network algorithm still stays in the end-to-end training supervision method like Image-Label pairs, which makes traditional algorithm is difficult to explain the reason for the results, and the prediction logic is difficult to understand and analyze.
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
Automated conversion methods are essential to overcome manual conversion challenges.
A multimodal gesture recognition dataset for desktop human-computer interaction
Gesture recognition is an indispensable component of natural and efficient human-computer interaction technology, particularly in desktop-level applications, where it can significantly enhance people's productivity.
Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment
The first KL divergence optimizes the conditional variational autoencoder and regularizes the encoder outputs, while the second KL divergence performs a self-distillation from the posterior path to the prior path, ensuring the consistency of decoder outputs.
Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview
In recent years, the rapid advancement and impressive capabilities of Large Language Models (LLMs) have been evident across various domains.
Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing
Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category.
Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition
With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction.
Optimizing and Fine-tuning Large Language Model for Urban Renewal
This study demonstrates the effectiveness and superiority of the joint fine-tuning method using Prefix and LoRA for ChatGLM in the urban renewal knowledge QA tasks.
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
In this paper, we present a scalable and efficient paradigm to address data sparsity and cold-start issues in CDR, named CDR-Adapter, by decoupling the original recommendation model from the mapping function, without requiring re-engineering the network structure.
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
Particularly, given the inexact initialization oracle, our regularization-based algorithms achieve the best of both worlds - optimal reproducibility and near-optimal gradient complexity - for minimization and minimax optimization.
DPZero: Private Fine-Tuning of Language Models without Backpropagation
The widespread practice of fine-tuning large language models (LLMs) on domain-specific data faces two major challenges in memory and privacy.
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device
This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device.
AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
3) AntM$^{2}$C provides 1 billion CTR data with 200 features, including 200 million users and 6 million items.
Graph Edit Distance Learning via Different Attention
To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs.
Explore and Tell: Embodied Visual Captioning in 3D Environments
To overcome this limitation, we propose a novel task called Embodied Captioning, which equips visual captioning models with navigation capabilities, enabling them to actively explore the scene and reduce visual ambiguity from suboptimal viewpoints.
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
Firstly, the previous definitions of robustness in trajectory prediction are ambiguous.
A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Designing protein mutants with high stability and activity is a critical yet challenging task in protein engineering.
FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation
Curvilinear object segmentation is critical for many applications.
Movie101: A New Movie Understanding Benchmark
Closer to real scenarios, the Movie Clip Narrating (MCN) task in our benchmark asks models to generate role-aware narration paragraphs for complete movie clips where no actors are speaking.
InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation
Existing metrics only provide a single score to measure caption qualities, which are less explainable and informative.
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification
The XAI assessment was conducted across 132 brain parcels, extracted from a combination of the Harvard-Oxford and AAL brain atlases, and compared to well-known pathological regions to measure adherence to domain knowledge.
MPMQA: Multimodal Question Answering on Product Manuals
Taking into account the length of product manuals and the fact that a question is always related to a small number of pages, MPMQA can be naturally split into two subtasks: retrieving most related pages and then generating multimodal answers.
Road Network Representation Learning: A Dual Graph based Approach
The constructed hypergraph would naturally capture the high-order relationships among roads with hyperedges.
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling
We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling.
Data Might be Enough: Bridge Real-World Traffic Signal Control Using Offline Reinforcement Learning
To address these challenges, we propose: (1) a cyclical offline dataset (COD), designed based on common real-world scenarios to facilitate easy collection; (2) an offline RL model called DataLight, capable of learning satisfactory control strategies from the COD; and (3) a method called Arbitrary To Cyclical (ATC), which can transform most RL-based methods into cyclical signal control.
Accommodating Audio Modality in CLIP for Multimodal Processing
In this paper, we extend the stateof-the-art Vision-Language model CLIP to accommodate the audio modality for Vision-Language-Audio multimodal processing.
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
3D Spatial Multimodal Knowledge Accumulation for Scene Graph Prediction in Point Cloud
In-depth understanding of a 3D scene not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them.
Towards Better Document-level Relation Extraction via Iterative Inference
Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, ignoring the fact that predictions of some entity pairs heavily depend on the predicted results of other pairs.
Region Embedding with Intra and Inter-View Contrastive Learning
Motivated by the success of contrastive learning for representation learning, we propose to leverage it for multi-view region representation learning and design a model called ReMVC (Region Embedding with Multi-View Contrastive Learning) by following two guidelines: i) comparing a region with others within each view for effective representation extraction and ii) comparing a region with itself across different views for cross-view information sharing.
Deep Reinforcement Learning with Vector Quantized Encoding
Human decision-making often involves combining similar states into categories and reasoning at the level of the categories rather than the actual states.
Using Features at Multiple Temporal and Spatial Resolutions to Predict Human Behavior in Real Time
We contend that for an artificially intelligent agent to effectively model human teammates, i. e., demonstrate computational theory of mind (ToM), it should do the same.
DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL
Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC).
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences.
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware.
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks.
The Future of Traditional Fuel Vehicles (TFV) and New Energy Vehicles (NEV): Creative Destruction or Co-existence?
There is a rapid development and commercialization of new Energy Vehicles (NEV) in recent years.
LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments
We present LinearAlifold, an efficient algorithm for folding aligned RNA homologs that scales linearly with both the sequence length and the number of sequences, based on our recent work LinearFold that folds a single RNA in linear time.
Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection
However, there is still room for improvement in video HOI detection performance.
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
We provide a general framework for solving differentially private stochastic minimax optimization (DP-SMO) problems, which enables the practitioners to bring their own base optimization algorithm and use it as a black-box to obtain the near-optimal privacy-loss trade-off.
Generalizing Multimodal Pre-training into Multilingual via Language Acquisition
Specifically, we design a lightweight language acquisition encoder based on state-of-the-art monolingual VLP models.
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
We further study the algorithm-dependent generalization bounds via stability arguments of algorithms.
Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks
The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation.
A Masked Image Reconstruction Network for Document-level Relation Extraction
After that, we look on the entity-pair matrix as an image and then randomly mask it and restore it through an inference module to capture the correlations between the relationships.
Ranked #3 on
Relation Extraction
on GDA
Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition
Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence.
A Token-level Contrastive Framework for Sign Language Translation
Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people.
Ranked #6 on
Sign Language Translation
on CSL-Daily
DynLight: Realize dynamic phase duration with multi-level traffic signal control
We would like to withdraw this article for the following reasons: 1 this article is not satisfactory for limited language and theoretical description; 2 we have enriched and revised this article with the help of other authors; 3 we must update the author contribution information.
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction
Document-level relation extraction (RE), which requires reasoning on multiple entities in different sentences to identify complex inter-sentence relations, is more challenging than sentence-level RE.
A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction
Specifically, the Dense-CCNet performs entity-pair-level logical reasoning through the Criss-Cross Attention (CCA), which can collect contextual information in horizontal and vertical directions on the entity-pair matrix to enhance the corresponding entity-pair representation.
Ranked #2 on
Relation Extraction
on CDR
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Scene Graph Generation: A Comprehensive Survey
In this paper, we provide a comprehensive survey of recent achievements in this field brought about by deep learning techniques.
Learning From Pixel-Level Noisy Label: A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization
We propose two new methods: (1) Max Queue-Length (M-QL), an optimization-based traditional method designed based on the property of queue length; and (2) AttentionLight, an RL model that employs the self-attention mechanism to capture the signal phase correlation without requiring human knowledge of phase relationships.
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem.
An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
Conversion rate (CVR) prediction is becoming in- creasingly important in the multi-billion dollar on- line display advertising industry.
Gram-SLD: Automatic Self-labeling and Detection for Instance Objects
Instance object detection plays an important role in intelligent monitoring, visual navigation, human-computer interaction, intelligent services and other fields.
Efficient Pressure: Improving efficiency for signalized intersections
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem.
Analysis and visualization of spatial transcriptomic data
Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners.
Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition
With the effective spatio-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also to directly capture inter-time dependencies.
Deep Natural Language Processing for LinkedIn Search
Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Deep Natural Language Processing for LinkedIn Search Systems
Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.
A Systematic Collection of Medical Image Datasets for Deep Learning
Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
Local $K$-means: An Efficient Optimization Algorithm And Its Generalization
We also developed a general model that unified LKM, KSUMS, and SC, and discussed the connection among them.
MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation
Second, we incorporate a novel interactive long-short term memory network (InLSTM) to reinforce the interactions of multilevel medical sequences in EHR data with the help of the calibrated memory-augmented cell and an enhanced input gate.
Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud
There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
Real-Time Vanishing Point Detector Integrating Under-Parameterized RANSAC and Hough Transform
We propose a novel approach that integrates under-parameterized RANSAC (UPRANSAC) with Hough Transform to detect vanishing points (VPs) from un-calibrated monocular images.
A two-component Comptonisation model for the type-B QPO in MAXI J1348-630
The data show a strong type-B QPO at ~4. 5 Hz with increasing fractional rms amplitude with energy and positive lags with respect to a reference band at 2-2. 5 keV.
High Energy Astrophysical Phenomena
Smoothed Gaussian Mixture Models for Video Classification and Recommendation
SGMM represents each video by the parameters of a Gaussian mixture model (GMM) trained for that video.
More Industry-friendly: Federated Learning with High Efficient Design
Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency.
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability.
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
To address this problem, we introduce a lightweight but effective Global Matching Component (GMC) to grab global matching features.
How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability.
DeText: A Deep Text Ranking Framework with BERT
Ranking is the most important component in a search system.
MeDaS: An open-source platform as service to help break the walls between medicine and informatics
To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side.
On Vocabulary Reliance in Scene Text Recognition
This remedy alleviates the problem of vocabulary reliance and improves the overall scene text recognition performance.
COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine.
Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma.
Adversarial Data Encryption
In the big data era, many organizations face the dilemma of data sharing.
Efficient Scene Text Detection with Textual Attention Tower
Scene text detection has received attention for years and achieved an impressive performance across various benchmarks.
Structure-Feature based Graph Self-adaptive Pooling
To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes.
Relation Graph Network for 3D Object Detection in Point Clouds
Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images.
Beyond Similarity: Relation Embedding with Dual Attentions for Item-based Recommendation
Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry in recent years.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
Point Attention Network for Semantic Segmentation of 3D Point Clouds
We propose a point attention network that learns rich local shape features and their contextual correlations for 3D point cloud semantic segmentation.
Adversarial Training Methods for Network Embedding
To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain.
Recommendation with Attribute-aware Product Networks: A Representation Learning Model
Specifically, eRAN first maps items connected in attribute networks to low-dimensional embedding vectors through a deep autoencoder, and then an attention mechanism is applied to model the attractions of attributes to users, from which personalized item representation can be derived.
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i. e., high-level agent and low-level agent.
Hierarchical Reinforcement Learning
Recommendation Systems
+2
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes.
Attention in Convolutional LSTM for Gesture Recognition
On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM.
Real-time Power System State Estimation and Forecasting via Deep Neural Networks
To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring.
SepNE: Bringing Separability to Network Embedding
We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes.
Deep Reinforcement Learning for Page-wise Recommendations
In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.
Deep Keyframe Detection in Human Action Videos
Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
Deep Reinforcement Learning for List-wise Recommendations
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential.
Randomized Block Frank-Wolfe for Convergent Large-Scale Learning
To circumvent the limitations of existing methods, the present work develops step sizes for RB-FW that enable a flexible selection of the number of blocks to update per iteration while ensuring convergence and feasibility of the iterates.
Hashtag Recommendation with Topical Attention-Based LSTM
Microblogging services allow users to create hashtags to categorize their posts.
Sparse Phase Retrieval via Truncated Amplitude Flow
Upon formulating sparse PR as an amplitude-based nonconvex optimization task, SPARTA works iteratively in two stages: In stage one, the support of the underlying sparse signal is recovered using an analytically well-justified rule, and subsequently, a sparse orthogonality-promoting initialization is obtained via power iterations restricted on the support; and, in the second stage, the initialization is successively refined by means of hard thresholding based gradient-type iterations.
Information Theory Information Theory Optimization and Control
Fast Computation of Posterior Mode in Multi-Level Hierarchical Models
Multi-level hierarchical models provide an attractive framework for incorporating correlations induced in a response variable organized in a hierarchy.
