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Found 22 papers, 4 papers with code
Common Ground Tracking in Multimodal Dialogue
Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on ``dialogue state tracking'' (DST), which is the ability to update the representations of the speaker's needs at each turn in the dialogue by taking into account the past dialogue moves and history.
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field.
Semantic Object-level Modeling for Robust Visual Camera Relocalization
Due to the improvement of CNN-based object detection algorithm, the robustness of visual relocalization is greatly enhanced especially in viewpoints where classical methods fail.
Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously
Availability attacks can prevent the unauthorized use of private data and commercial datasets by generating imperceptible noise and making unlearnable examples before release.
Detection and Defense of Unlearnable Examples
Detectability of unlearnable examples with simple networks motivates us to design a novel defense method.
Adapting to climate change: Long-term impact of wind resource changes on China's power system resilience
Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change.
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors.
Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation
Besides, different artists may use diverse drawing techniques and create multiple styles of sketches; but the style is globally consistent in a sketch.
Continual Learning as Computationally Constrained Reinforcement Learning
The design of such agents, which remains a long-standing challenge of artificial intelligence, is addressed by the subject of continual learning.
Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections
To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering.
Consistent Depth Prediction for Transparent Object Reconstruction from RGB-D Camera
We compare the metrics on our dataset and SLAM reconstruction results in both synthetic scenes and real scenes with the previous methods.
Is Stochastic Gradient Descent Near Optimal?
However, existing computational theory suggests that, even for single-hidden-layer teacher networks, to attain small error for all such teacher networks, the computation required to achieve this sample complexity is intractable.
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings
In this paper, we revisit the long-standing problem of automatic reconstruction of 3D objects from single line drawings.
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword.
Click-Through Rate Prediction
Graph Representation Learning
+1
An Information-Theoretic Framework for Supervised Learning
For a particular prior distribution on weights, we establish sample complexity bounds that are simultaneously width independent and linear in depth.
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL).
Fully Three-dimensional Radial Visualization
We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets.
Heterogeneous Graph-Based Multimodal Brain Network Learning
Other experiments also indicate that our proposed model with a pretraining strategy alleviates the problem of limited labelled data and yields a significant improvement in accuracy.
Systematic design and experimental demonstration of transmission-type multiplexed acoustic meta-holograms
Acoustic holograms have promising applications in sound-field reconstruction, particle manipulation, ultrasonic haptics and therapy.
Applied Physics
Transfusion: A Novel SLAM Method Focused on Transparent Objects
In this paper, we propose a novel SLAM approach called transfusion that allows transparent object existence and recovery in the video input.
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.
Visualization of Labeled Mixed-featured Datasets
We develop methodology for visualization of labeled mixed-featured datasets.
