6 papers
Intermediate Representations are Strong AI-Generated Image Detectors
Zhenhan Huang, Pin-Yu Chen, Tejaswini Pedapati +1
The rapid advancement in generative AI models has enabled the creation of photorealistic images. At the same time, there are growing concerns about the potential misuse and dangers…
MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling
MiroMind Team, Song Bai, Lidong Bing +52
We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale…
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz +7
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful…
Graph is all you need? Lightweight data-agnostic neural architecture search without training
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +2
Neural architecture search (NAS) enables the automatic design of neural network models. However, training the candidates generated by the search algorithm for performance evaluatio…
Modular Prompt Learning Improves Vision-Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encode…
Differentiable Prompt Learning for Vision Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning th…