Search Results for author:
Found 28 papers, 5 papers with code
Multi-resolution Annotations for Emoji Prediction
The lack of multi-label and aspect-level emoji prediction datasets is one of the bottlenecks for this task.
QCS:Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
On facial expression datasets with complex and numerous feature types, where the significance and dominance of labeled features are difficult to predict, facial expression recognition(FER) encounters the challenges of inter-class similarity and intra-class variances, making it difficult to mine effective features.
Facial Expression Recognition
Facial Expression Recognition (FER)
Distributed Resilient Secondary Control for Microgrids with Attention-based Weights against High-density Misbehaving Agents
Microgrids (MGs) have been equipped with large-scale distributed energy sources (DESs), and become more vulnerable due to the low inertia characteristic.
Distributed Deep Koopman Learning for Nonlinear Dynamics
Koopman operator theory has proven to be highly significant in system identification, even for challenging scenarios involving nonlinear time-varying systems (NTVS).
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model
Hence, as an example to present how to overcome the issue, we built a framework for general analysis of galaxy images, based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more.
Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials
Mechanical metamaterial is a synthetic material that can possess extraordinary physical characteristics, such as abnormal elasticity, stiffness, and stability, by carefully designing its internal structure.
Feature-Suppressed Contrast for Self-Supervised Food Pre-training
As the similar contents of the two views are salient or highly responsive in the feature map, the proposed FeaSC uses a response-aware scheme to localize salient features in an unsupervised manner.
Graph-Level Embedding for Time-Evolving Graphs
We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods.
Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks.
Split-Spectrum Based Distributed State Estimation for Linear Systems
A discrete-time version of the distributed state estimation problem is also studied, and a corresponding estimator based again on spectrum separation, but not high gain, is proposed for time-varying networks.
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English
While cultural backgrounds have been shown to affect linguistic expressions, existing natural language processing (NLP) research on culture modeling is overly coarse-grained and does not examine cultural differences among speakers of the same language.
Cultural Vocal Bursts Intensity Prediction
Language Modelling
+5
Embedding Node Structural Role Identity Using Stress Majorization
In this paper, we present a novel and flexible framework using stress majorization, to transform the high-dimensional role identities in networks directly (without approximation or indirect modeling) to a low-dimensional embedding space.
Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
Recent years have seen a rise in the development of representational learning methods for graph data.
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks
Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0. 33% to 17. 93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks.
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks
Through extensive experiments, we show that (1) pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks and (2) the attention heads to be pruned can be ranked using gradients and identified with a few trial experiments.
Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition
In this paper, we analyze the difficulties of fine-grained image recognition from a new perspective and propose a transformer architecture with the peak suppression module and knowledge guidance module, which respects the diversification of discriminative features in a single image and the aggregation of discriminative clues among multiple images.
Ranked #8 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Image Recognition
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
A common type of network is the heterogeneous network, where the nodes (and edges) can be of different types.
Mitigating Political Bias in Language Models Through Reinforced Calibration
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings.
Political Depolarization of News Articles Using Attribute-aware Word Embeddings
To detect polar words, we train a multi-attribute-aware word embedding model that is aware of ideology and topics on 360k full-length media articles.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
Improvements and Extensions on Metaphor Detection
Finally, we clean up the improper or outdated annotations in one of the MD benchmark datasets and re-benchmark it with our Transformer-based model.
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data
The field of NLP has seen unprecedented achievements in recent years.
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
For instance, Data Boost improves F1 for the three tasks by 8. 7% on average when given only 10% of the whole data for training.
Embedding Node Structural Role Identity into Hyperbolic Space
However, the work on network embedding in hyperbolic space has been focused on microscopic node embedding.
Emoji Prediction: Extensions and Benchmarking
In this paper, we extend the existing setting of the emoji prediction task to include a richer set of emojis and to allow multi-label classification on the task.
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks
Once trained on their accounts, users can have new photos sorted based on predicted engagement and style similarity to their previous work, thus enabling them to upload photos that not only have the potential to maximize engagement from their followers but also maintain their style of photography.
Distributed Feedback Control of Multi-Channel Linear Systems
In this paper it is established that any jointly controllable, jointly observable, multi-channel, discrete or continuous time linear system with a strongly connected neighbor (communication) graph can be exponentially stabilized with any pre-specified convergence rate using a time-invariant distributed linear control.
A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards
The integration and application of maxcombo tests in interim analyses often require extensive simulation studies.
Methodology
