Search Results for author:
Found 7 papers, 1 papers with code
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
Recently, SSL has been a new trend in exploring the representation learning capability in the realm of tabular data, which is more challenging due to not having explicit relations for learning descriptive representations.
Unveiling Invariances via Neural Network Pruning
Invariance describes transformations that do not alter data's underlying semantics.
Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search
In this paper, we propose NSUBS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network.
Ray Based Distributed Autonomous Vehicle Research Platform
Carla is a vehicle simulator that generates data used to train a model.
GLSearch: Maximum Common Subgraph Detection via Learning to Search
However, MCS computation is NP-hard, and state-of-the-art MCS solvers rely on heuristic search algorithms which in practice cannot find good solution for large graph pairs given a limited computation budget.
Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction
Maximum Common Subgraph (MCS) is defined as the largest subgraph that is commonly present in both graphs of a graph pair.
