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Found 25 papers, 9 papers with code
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.
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.
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).
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.
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning
This minimizes the interference between parameters for different tasks.
Ranked #5 on
Continual Learning
on Tiny-ImageNet (10tasks)
Active Learning by Feature Mixing
We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations.
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.
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).
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
Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift
Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data.
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.
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.
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.
Stock Market Prediction via Deep Learning Techniques: A Survey
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods.
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
In this paper, we revisit the idea of using image reconstruction as the auxiliary task and incorporate it with a modern semi-supervised semantic segmentation framework.
Semantic Role Labeling Guided Out-of-distribution Detection
Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications.
Factor Graph Neural Networks
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications.
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources.
Influence Function Based Second-Order Channel Pruning-Evaluating True Loss Changes For Pruning Is Possible Without Retraining
It motivates us to develop a technique to evaluate true loss changes without retraining, with which channels to prune can be selected more reliably and confidently.
Identifiable Latent Polynomial Causal Models Through the Lens of Change
However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
To address this limitation, we adopt a causal generative perspective for multimodal data and propose contrastive learning with data augmentation to disentangle content features from the original representations.
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box.
Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models
Multimodal contrastive representation learning methods have proven successful across a range of domains, partly due to their ability to generate meaningful shared representations of complex phenomena.
A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples.
Identifiable Latent Neural Causal Models
This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models.
