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Found 14 papers, 5 papers with code
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature.
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal.
Truncated Matrix Power Iteration for Differentiable DAG Learning
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.
Identifying Weight-Variant Latent Causal Models
The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.
Identifying Latent Causal Content for Multi-Source Domain Adaptation
This motivates us to propose a novel method for MSDA, which learns the invariant label distribution conditional on the latent content variable, instead of learning invariant representations.
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system.
Ranked #1 on
Stock Price Prediction
on Astock
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Implicit Sample Extension for Unsupervised Person Re-Identification
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
Active Learning by Feature Mixing
We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations.
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning
This minimizes the interference between parameters for different tasks.
Memory-Augmented Dynamic Neural Relational Inference
This motivates us to propose a memory-augmented dynamic neural relational inference method, which maintains two associative memory pools: one for the interactive relations and the other for the individual entities.
Consistency-Aware Graph Network for Human Interaction Understanding
Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU).
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation
Through empirical studies, we show that our agent, dubbed as the optimistic agent, has a more realistic estimate of the state value during a navigation episode which leads to a higher success rate.
Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead
Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent.
