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Found 29 papers, 10 papers with code
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information.
PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval
In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners.
Prompt-based Personalized Federated Learning for Medical Visual Question Answering
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods.
Medical Visual Question Answering
Personalized Federated Learning
+2
Bidirectional Generative Pre-training for Improving Time Series Representation Learning
Learning time-series representations for discriminative tasks has been a long-standing challenge.
Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents
A stack-based LLM plan generation process is then conducted under the supervision of the automaton to ensure that the generated plan satisfies the constraints, making the planning process controllable.
SAM-guided Graph Cut for 3D Instance Segmentation
Experimental results on the ScanNet, ScanNet++ and KITTI-360 datasets demonstrate that our method achieves robust segmentation performance and can generalize across different types of scenes.
Attention Schema in Neural Agents
A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents.
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.
Adaptive Data Augmentation for Contrastive Learning
In computer vision, contrastive learning is the most advanced unsupervised learning framework.
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively.
Interpretable Visual Question Answering Referring to Outside Knowledge
We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks.
Stargazer: A transformer-based driver action detection system for intelligent transportation
Distracted driver actions can be dangerous and cause severe accidents.
Graph Collaborative Reasoning
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.
Differentiable Synthesis of Program Architectures
Differentiable programs have recently attracted much interest due to their interpretability, compositionality, and their efficiency to leverage differentiable training.
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets.
Hierarchical information matters: Text classification via tree based graph neural network
Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification.
Programmatic Reinforcement Learning without Oracles
Our first contribution is a programmatically interpretable RL framework that conducts program architecture search on top of a continuous relaxation of the architecture space defined by programming language grammar rules.
Intra-Class Uncertainty Loss Function for Classification
To address this issue, we propose a loss function with intra-class uncertainty following Gaussian distribution.
Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation
In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation.
Patient similarity: methods and applications
Patient similarity analysis is important in health care applications.
FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network
The concept of disinformation is to use fake messages to confuse people in order to protect the real information.
Robustness to Adversarial Attacks in Learning-Enabled Controllers
We consider shield-based defenses as a means to improve controller robustness in the face of such perturbations.
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many institutions in the process.
ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks
Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process and operate over dynamically constructed partitions of the input space that considers accuracy and safety objectives synergistically.
An Inductive Synthesis Framework for Verifiable Reinforcement Learning
Rather than enforcing safety by examining and altering the structure of a complex neural network implementation, our technique uses blackbox methods to synthesizes deterministic programs, simpler, more interpretable, approximations of the network that can nonetheless guarantee desired safety properties are preserved, even when the network is deployed in unanticipated or previously unobserved environments.
Distributed Data Vending on Blockchain
However, one key challenge in distributed data vending is the trade-off dilemma between the effectiveness of data retrieval, and the leakage risk from indexing the data.
