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Found 16 papers, 8 papers with code
Elevating Spectral GNNs through Enhanced Band-pass Filter Approximation
Spectral Graph Neural Networks (GNNs) have attracted great attention due to their capacity to capture patterns in the frequency domains with essential graph filters.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Deep quantization methods have shown high efficiency on large-scale image retrieval.
Spectral GNN via Two-dimensional (2-D) Graph Convolution
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph learning.
Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation
Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings.
PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors.
CLEX: Continuous Length Extrapolation for Large Language Models
We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths.
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks.
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective.
Revisiting Parameter-Efficient Tuning: Are We Really There Yet?
Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs).
Structure-Aware Random Fourier Kernel for Graphs
Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels.
Variational Continual Bayesian Meta-Learning
Conventional meta-learning considers a set of tasks from a stationary distribution.
Addressing Class-Imbalance Problem in Personalized Ranking
Pairwise ranking models have been widely used to address recommendation problems.
Semi-supervisedly Co-embedding Attributed Networks
Deep generative models (DGMs) have achieved remarkable advances.
Variational Self-attention Model for Sentence Representation
This paper proposes a variational self-attention model (VSAM) that employs variational inference to derive self-attention.
Neural Variational Hybrid Collaborative Filtering
Specifically, we consider both the generative processes of users and items, and the prior of latent factors of users and items to be side informationspecific, which enables our model to alleviate matrix sparsity and learn better latent representations of users and items.
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.
