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Found 18 papers, 10 papers with code
Benchmarking Meaning Representations in Neural Semantic Parsing
Thus, the impact of meaning representation on semantic parsing is less understood.
OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks.
Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects
To assure high reliability against numerical defects, in this paper, we propose the RANUM approach including novel techniques for three reliability assurance tasks: detection of potential numerical defects, confirmation of potential-defect feasibility, and suggestion of defect fixes.
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Local feature matching is an essential component in many visual applications.
DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching
In this work, we propose DeepMatcher, a deep Transformer-based network built upon our investigation of local feature matching in detector-free methods.
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once
In this work, we show that it is feasible to perform multiple tasks concurrently on point cloud with a straightforward yet effective multi-task network.
MDL-NAS: A Joint Multi-Domain Learning Framework for Vision Transformer
Technically, MDL-NAS constructs a coarse-to-fine search space, where the coarse search space offers various optimal architectures for different tasks while the fine search space provides fine-grained parameter sharing to tackle the inherent obstacles of multi-domain learning.
Double Sampling Randomized Smoothing
To overcome this hurdle, we propose a Double Sampling Randomized Smoothing (DSRS) framework, which exploits the sampled probability from an additional smoothing distribution to tighten the robustness certification of the previous smoothed classifier.
On the Certified Robustness for Ensemble Models and Beyond
Thus, to explore the conditions that guarantee to provide certifiably robust ensemble ML models, we first prove that diversified gradient and large confidence margin are sufficient and necessary conditions for certifiably robust ensemble models under the model-smoothness assumption.
SoK: Certified Robustness for Deep Neural Networks
Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on a wide range of tasks.
Adversarial Attack on Large Scale Graph
Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance.
Understanding Challenges in Deploying Deep Learning Based Software: An Empirical Study
Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software applications.
Software Engineering
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
TSS: Transformation-Specific Smoothing for Robustness Certification
Moreover, to the best of our knowledge, TSS is the first approach that achieves nontrivial certified robustness on the large-scale ImageNet dataset.
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications
Recent research proposes syntax-based approaches to address the problem of generating programs from natural language specifications.
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks
In particular, given a target model, our framework includes multiple models (constructed from the target model) to form a model family.
Testing Untestable Neural Machine Translation: An Industrial Case
To fill the gap of lacking test oracle for in-vivo testing of an NMT system, in this paper, we propose a new approach for automatically identifying translation failures, without requiring reference translations for a translation task; our approach can directly serve as a test oracle for in-vivo testing.
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