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Found 16 papers, 4 papers with code
Modelling Sentence Pairs with Tree-structured Attentive Encoder
We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs.
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications.
Towards High-Order Complementary Recommendation via Logical Reasoning Network
Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation.
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching
The unprecedented demand for large amount of data has catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently.
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods.
Learning Efficient Video Representation with Video Shuffle Networks
To this end, we propose the video shuffle, a parameter-free plug-in component that efficiently reallocates the inputs of 2D convolution so that its receptive field can be extended to the temporal dimension.
DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving
We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy.
Robust Federated Learning for Neural Networks
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
GAN-based Recommendation with Positive-Unlabeled Sampling
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.
A Neural Vocoder Based Packet Loss Concealment Algorithm
The packet loss problem seriously affects the quality of service in Voice over IP (VoIP) sceneries.
Retrieval-based Knowledge Augmented Vision Language Pre-training
With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi-modal downstream tasks.
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation
Then we learn a semantic representation of MeKB for the cross-domain recommendation.
Side Information-Driven Session-based Recommendation: A Survey
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
HEAT: Head-level Parameter Efficient Adaptation of Vision Transformers with Taylor-expansion Importance Scores
In this paper, we propose Head-level Efficient Adaptation with Taylor-expansion importance score (HEAT): a simple method that efficiently fine-tuning ViTs at head levels.
