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Found 18 papers, 5 papers with code
An interpretability framework for Similar case matching
Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity.
Model and Evaluation: Towards Fairness in Multilingual Text Classification
The multilingual text representation module uses a multilingual pre-trained language model to represent the text, the language fusion module makes the semantic spaces of different languages tend to be consistent through contrastive learning, and the text debiasing module uses contrastive learning to make the model unable to identify sensitive attributes' information.
Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval
Our method utilizes a SciBERT as a text encoder and a graph transformer network as a molecule encoder to generate multimodal representations.
Ranked #1 on
Cross-Modal Retrieval
on ChEBI-20
An Effective Deployment of Contrastive Learning in Multi-label Text Classification
We explore the effectiveness of contrastive learning for multi-label text classification tasks by the employment of these novel losses and provide a set of baseline models for deploying contrastive learning techniques on specific tasks.
Temporally and Spatially variant-resolution illumination patterns in computational ghost imaging
For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than CGI, but it is less robust to noise.
Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning
Active visual tracking of space non-cooperative object is significant for future intelligent spacecraft to realise space debris removal, asteroid exploration, autonomous rendezvous and docking.
3D Visual Tracking Framework with Deep Learning for Asteroid Exploration
3D visual tracking is significant to deep space exploration programs, which can guarantee spacecraft to flexibly approach the target.
Complementary Fourier single-pixel imaging
The complementary nature of Fourier patterns based on a four-step phase-shift algorithm is combined with the complementary nature of a digital micromirror device.
Optimization of retina-like illumination patterns in ghost imaging
This proposed method is verified by simulations and experiments compared with conventional GI, retina-like GI and GI using patterns optimized by principal component analysis.
Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.
Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
Universal Trading for Order Execution with Oracle Policy Distillation
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
GenAD: General Representations of Multivariate Time Series for Anomaly Detection
However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information
Our second method refines word representations by aligning original and re-fined embedding spaces based on local tangent space instead of performing weighted locally linear combination twice.
Qlib: An AI-oriented Quantitative Investment Platform
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
Scaling Nakamoto Consensus to Thousands of Transactions per Second
The Conflux consensus protocol represents relationships between blocks as a direct acyclic graph and achieves consensus on a total order of the blocks.
Distributed, Parallel, and Cluster Computing
Driving maneuvers prediction based on cognition-driven and data-driven method
To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.
