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Found 82 papers, 28 papers with code
PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning
From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research.
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation
Additionally, we also investigate the possible benefits of using calibrated intensity in semantic segmentation in urban environments (SemanticKITTI) and cross-sensor domain adaptation.
3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization
This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting.
Intersectional Two-sided Fairness in Recommendation
Fairness of recommender systems (RS) has attracted increasing attention recently.
Future Impact Decomposition in Request-level Recommendations
Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.
Off-Road LiDAR Intensity Based Semantic Segmentation
LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning.
GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction
To our knowledge, this research is the first to conduct both quantitative and qualitative evaluations of VLMs in the context of pedestrian behavior prediction for autonomous driving.
ROSS: Radar Off-road Semantic Segmentation
As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential.
AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement
One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.
KuaiSim: A Comprehensive Simulator for Recommender Systems
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
Discrete Conditional Diffusion for Reranking in Recommendation
To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation.
A Large Language Model Enhanced Conversational Recommender System
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
Measuring Item Global Residual Value for Fair Recommendation
In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems.
State Regularized Policy Optimization on Data with Dynamics Shift
Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
Generative Flow Network for Listwise Recommendation
In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.
Improving Extrinsics between RADAR and LIDAR using Learning
This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems.
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint
Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution.
Exploration and Regularization of the Latent Action Space in Recommendation
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results.
Multi-Task Recommendations with Reinforcement Learning
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness.
Two-Stage Constrained Actor-Critic for Short Video Recommendation
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
Reinforcing User Retention in a Billion Scale Short Video Recommender System
In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.
Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision
Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation.
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.
Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training
At higher sparsity, our algorithm can still match the accuracy of nonstructured sparse training in most cases, while reducing the training time by up to 5x due to the fine-grained block structures in the models.
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification
Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time.
Real-time Short Video Recommendation on Mobile Devices
However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
To facilitate model learning, we further collect rich features of users and items as well as users' behavior history.
The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems
In this paper, we propose an improved column generation algorithm with neural prediction (CG-P) for solving graph-based set covering problems.
Contrastive Learning of Features between Images and LiDAR
Moreover, we conduct experiments on a real-world dataset to show the effectiveness of our loss function and network structure.
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction -- the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved.
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e. g., click, like and purchase).
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models
By assigning different weights to each model for different inputs, we adopted the Transformer layer to integrate the advantages of diverse models effectively.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Constrained Reinforcement Learning for Short Video Recommendation
In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.
A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels
We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.
Deep Learning based Intelligent Coin-tap Test for Defect Recognition
This paper further develops transfer learning strategies for this issue, that is, to transfer the model trained on data of one scenario to another.
Local neural operator for solving transient partial differential equations on varied domains
For demonstration, LNO learns Navier-Stokes equations from randomly generated data samples, and then the pre-trained LNO is used as an explicit numerical time-marching scheme to solve the flow of fluid on unseen domains, e. g., the flow in a lid-driven cavity and the flow across the cascade of airfoils.
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.
Double-Barreled Question Detection at Momentive
We concluded that the word2vec subword embedding with maximum pooling is the optimal word embedding representation in terms of precision and running time in the offline experiments using the survey data at Momentive.
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
The proposed approach is currently serving over hundreds of millions of users on the platform and achieves one of the most tremendous improvements over these months.
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion.
Ranked #1 on
Recommendation Systems
on ReDial
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
SpSC: A Fast and Provable Algorithm for Sampling-Based GNN Training
However, we find that SCO algorithms are impractical for training GNNs on large graphs because they need to store the moving averages of the aggregated features of all nodes in the graph.
SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator
This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information.
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.
Calibrating LiDAR and Camera using Semantic Mutual information
We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information.
OFFSEG: A Semantic Segmentation Framework For Off-Road Driving
Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures.
Extragalactic HI 21-cm absorption line observations with the Five-hundred-meter Aperture Spherical radio Telescope
Our observations successfully confirmed the existence of HI absorption lines in all these systems, including two sources that were marginally detected by ALFALFA.
Astrophysics of Galaxies
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace
Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations.
Graph Attention Collaborative Similarity Embedding for Recommender System
We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning.
Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration
Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications.
Graph Mining
Databases
Performance
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks
However, we found that the existing neighbor sampling methods do not work well in a distributed setting.
RELLIS-3D Dataset: Data, Benchmarks and Analysis
The data was collected on the Rellis Campus of Texas A\&M University and presents challenges to existing algorithms related to class imbalance and environmental topography.
Ranked #1 on
3D Semantic Segmentation
on RELLIS-3D Dataset
Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace
Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts.
NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation
To modeling the implicit correlations of neighbors in graph embedding aggregating, we propose a Neighbor-Aware Graph Attention Network for recommendation task, termed NGAT4Rec.
Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning
Compared with full-communication SGD, our ADPSGD achieves 1:14x to 1:27x speedups with a 100Gbps connection among computing nodes, and the speedups increase to 1:46x ~ 1:95x with a 10Gbps connection.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.
Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation.
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds
Instance segmentation in point clouds is one of the most fine-grained ways to understand the 3D scene.
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation
We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet).
GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining
The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries.
Learning Representations of Categorical Feature Combinations via Self-Attention
In most current DNN based models, feature embeddings are simply concatenated for further processing by networks.
Compositional Network Embedding
Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.
Personalized Re-ranking for Recommendation
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.
Ranked #2 on
Recommendation Systems
on MovieLens 1M
(HR@10 (full corpus) metric)
Deep Learning Inversion of Electrical Resistivity Data
According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.
Ranking in Genealogy: Search Results Fusion at Ancestry
Genealogy research is the study of family history using available resources such as historical records.
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.
Deep-Learning Inversion of Seismic Data
We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i. e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs).
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
The large communication overhead has imposed a bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) for training deep neural networks.
Super Diffusion for Salient Object Detection
Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection.
Multi-Source Pointer Network for Product Title Summarization
For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.
DifNet: Semantic Segmentation by Diffusion Networks
Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions.
DiDA: Disentangled Synthesis for Domain Adaptation
The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.
Generic Promotion of Diffusion-Based Salient Object Detection
In this work, we propose a generic scheme to promote any diffusion-based salient object detection algorithm by original ways to re-synthesize the diffusion matrix and construct the seed vector.
