Search Results for author:
Found 15 papers, 7 papers with code
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks
Previous works on word-level attacks widely use word importance ranking (WIR) methods and complex search methods, including greedy search and heuristic algorithms, to find optimal substitutions.
TempCompass: Do Video LLMs Really Understand Videos?
Motivated by these two problems, we propose the \textbf{TempCompass} benchmark, which introduces a diversity of temporal aspects and task formats.
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information.
Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search
It suffers from the large-scale $\mathcal{X}$ because a PG with full vectors is too large to fit into the memory, e. g., a billion-scale $\mathcal{X}$ in 128 dimensions would consume nearly 600 GB memory.
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing
The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption.
DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex
The main drawback of graph-based ANNS is that a graph index would be too large to fit into the memory especially for a large-scale $\mathcal{X}$.
A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets
In order to solve the problem of non-ideal training sets (i. e., the less-complete or over-complete sets) and implement one-iteration learning, a novel efficient quantum perceptron algorithm based on unitary weights is proposed, where the singular value decomposition of the total weight matrix from the training set is calculated to make the weight matrix to be unitary.
AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder
To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder.
HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields
In this work, we propose to use neural fields, a differentiable representation of functions through neural networks, to model HRTFs with arbitrary spatial sampling schemes.
Predicting Global Head-Related Transfer Functions From Scanned Head Geometry Using Deep Learning and Compact Representations
For the HRTF data, we use truncated spherical harmonic (SH) coefficients to represent the HRTF magnitudes and onsets.
Navigable Proximity Graph-Driven Native Hybrid Queries with Structured and Unstructured Constraints
We easily deploy existing various PGs on this framework to process hybrid queries efficiently.
COVID-19 Fake News Detection Using Bidirectional Encoder Representations from Transformers Based Models
The model performance evaluation results showcase that our best model (BERT finetuned model with frozen parameters plus BiLSTM layers) achieves state-of-the-art results towards COVID-19 fake news detection task.
Identifying modulation formats using integrated clustering algorithm
Modulation format identification (MFI) is crucial in next-generation optical networks such as cognitive optical networks.
A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search
Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition.
