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Cesar Ilharco Magalhaes

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    Orchestrating Synthetic Data with Reasoning
    Tim Davidson
    Benoit Seguin
    Enrico Bacis
    Cesar Magalhaes
    Hamza Harkous
    SynthData @ ICLR2025
    Preview abstract Many AI applications of interest require specialized multi-modal models. Yet, relevant data for training these models is inherently scarce. Human annotation is prohibitively expensive, error-prone, and time-consuming. Meanwhile, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution - limiting scalability and control. In this paper, we introduce Simula, a novel, seedless framework that balances global and local reasoning to generate synthetic datasets. We utilize taxonomies to capture a global coverage space and use a series of agentic refinements to promote local diversity and complexity. Our approach allows users to define desired dataset characteristics through an explainable and controllable process, without relying on seed data. This unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount. View details
    Recognizing Multimodal Entailment (tutorial at ACL 2021)
    Afsaneh Hajiamin Shirazi
    Arjun Gopalan
    Arsha Nagrani
    Blaž Bratanič
    Cesar Ilharco Magalhaes
    Christina Liu
    Felipe Ferreira (Goldstein)‎
    Gabriel Fedrigo Barcik
    Georg Fritz Osang
    Jared Frank
    Lucas Smaira
    Qin Cao
    Ricardo Abasolo Marino
    Roma Patel
    Thomas Leung
    Vaiva Imbrasaite
    (2021) (to appear)
    Preview abstract How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web. With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in machine learning. This cutting-edge tutorial aims to introduce the multimodal entailment task, which can be useful for detecting semantic alignments when a single modality alone does not suffice for a whole content understanding. Starting with a brief overview of natural language processing, computer vision, structured data and neural graph learning, we lay the foundations for the multimodal sections to follow. We then discuss recent multimodal learning literature covering visual, audio and language streams, and explore case studies focusing on tasks which require fine-grained understanding of visual and linguistic semantics question answering, veracity and hatred classification. Finally, we introduce a new dataset for recognizing multimodal entailment, exploring it in a hands-on collaborative section. Overall, this tutorial gives an overview of multimodal learning, introduces a multimodal entailment dataset, and encourages future research in the topic. View details
    Neural Structured Learning in TensorFlow: Hands-On Tutorial at KDD
    Arjun Gopalan
    Da-Cheng Juan
    Cesar Ilharco Magalhaes
    Chun-Sung Ferng
    Allan Heydon
    Chun-Ta Lu
    Philip Minh Pham
    George Yu
    (2020), pp. 3501-3502
    Preview abstract We present Neural Structured Learning (NSL) in TensorFlow, a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials. View details
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