Search Results for author:
Found 15 papers, 8 papers with code
Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
In model-based reinforcement learning, simulated experiences from the learned model are often treated as equivalent to experience from the real environment.
Model-based Reinforcement Learning
reinforcement-learning
+1
Question Answering Over Spatio-Temporal Knowledge Graph
While the research community's focus on Knowledge Graph Question Answering (KGQA), the field of answering questions incorporating both spatio-temporal information based on STKGs remains largely unexplored.
On the Permanence of Backdoors in Evolving Models
Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely.
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs
The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph.
Ranked #1 on
Knowledge Base Question Answering
on LC-QuAD 1.0
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive.
Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base
However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries.
Ranked #2 on
Knowledge Base Question Answering
on LC-QuAD 1.0
Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks
In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns.
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.
Piracy Resistant Watermarks for Deep Neural Networks
We empirically show that our proposed watermarks achieve piracy resistance and other watermark properties, over a wide range of tasks and models.
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks
Recent work has proposed the concept of backdoor attacks on deep neural networks (DNNs), where misbehaviors are hidden inside "normal" models, only to be triggered by very specific inputs.
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
We identify multiple mitigation techniques via input filters, neuron pruning and unlearning.
Sparse representation-based over-sampling technique for classification of imbalanced dataset
As one of the most popular research fields in machine learning, the research on imbalanced dataset receives more and more attentions in recent years.
