Search Results for author:
Found 11 papers, 4 papers with code
Interpretable Failure Detection with Human-Level Concepts
Reliable failure detection holds paramount importance in safety-critical applications.
Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality
In these domains, prediction rationality is crucial: the prediction should be correct and based on valid evidence.
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales
Second, we propose a rationale-informed optimization method to guide the model in disentangling and localizing visual evidence for each rationale, without requiring manual annotations.
DEAL: Disentangle and Localize Concept-level Explanations for VLMs
Large pre-trained Vision-Language Models (VLMs) have become ubiquitous foundational components of other models and downstream tasks.
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
To achieve OOF-resiliency in a scalable manner, we propose Topology-aware Federated Learning (TFL) that leverages client topology - a graph representing client relationships - to effectively train robust models against OOF data.
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents
This approach aims to generate relation representations that are more aware of the spatial context and unseen relation in a manner similar to human perception.
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
Are Data-driven Explanations Robust against Out-of-distribution Data?
How to develop robust explanations against out-of-distribution data?
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
In this paper, we propose to use intermediate bottleneck features (IBFs) to replace PPGs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Learning for Spatiotemporal Modeling of Urbanization
Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization.
Application-level Studies of Cellular Neural Network-based Hardware Accelerators
While it is well-known that CeNNs can be well-suited for spatio-temporal information processing, few (if any) studies have quantified the energy/delay/accuracy of a CeNN-friendly algorithm and compared the CeNN-based approach to the best von Neumann algorithm at the application level.
