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Related papers: Instilling Inductive Biases with Subnetworks

200 papers

A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation. The field of meta-learning has provided a rich body of work describing effective forms of data characterization…

Machine Learning · Computer Science 2021-02-01 Mikhail M. Meskhi , Adriano Rivolli , Rafael G. Mantovani , Ricardo Vilalta

Learning automatically the best activation function for the task is an active topic in neural network research. At the moment, despite promising results, it is still difficult to determine a method for learning an activation function that…

Machine Learning · Computer Science 2019-10-29 Andrea Apicella , Francesco Isgrò , Roberto Prevete

Societal biases are reflected in large pre-trained language models and their fine-tuned versions on downstream tasks. Common in-processing bias mitigation approaches, such as adversarial training and mutual information removal, introduce…

Machine Learning · Computer Science 2023-06-06 Lukas Hauzenberger , Shahed Masoudian , Deepak Kumar , Markus Schedl , Navid Rekabsaz

In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we use different techniques to map instances into a similarity…

Machine Learning · Computer Science 2024-11-11 Samira Maghool , Paolo Ceravolo

Most prior research in deep imitation learning has predominantly utilized fixed cameras for image input, which constrains task performance to the predefined field of view. However, enabling a robot to actively maneuver its neck can…

Robotics · Computer Science 2025-06-24 Koki Nakagawa , Yoshiyuki Ohmura , Yasuo Kuniyoshi

The remarkable success of modern AI has been closely tied to scaling laws, yet the finite supply of high-quality data makes data efficiency--learning more from less--an increasingly important frontier. A model's inductive bias is a critical…

Machine Learning · Computer Science 2026-05-06 Qiyu Chen , Guozhang Chen

The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however it remains unclear whether this is sufficient to…

Machine Learning · Computer Science 2023-04-21 Simon Segert , Jonathan Cohen

The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as…

Robotics · Computer Science 2022-06-07 Haimin Hu , Jaime F. Fisac

Both humans and large language models are able to learn language without explicit structural supervision. What inductive biases make this learning possible? We address this fundamental cognitive question by leveraging transformer language…

Computation and Language · Computer Science 2023-10-31 Isabel Papadimitriou , Dan Jurafsky

Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to…

Artificial Intelligence · Computer Science 2018-01-03 Christopher Grimm , Dilip Arumugam , Siddharth Karamcheti , David Abel , Lawson L. S. Wong , Michael L. Littman

Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better generalization. In this work, we study the inductive bias and…

Machine Learning · Computer Science 2021-03-23 Yuchen Lu , Yikang Shen , Siyuan Zhou , Aaron Courville , Joshua B. Tenenbaum , Chuang Gan

Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately.…

Methodology · Statistics 2023-08-11 Snigdha Panigrahi , Natasha Stewart , Chandra Sekhar Sripada , Elizaveta Levina

Existing approaches to reward inference from behavior typically assume that humans provide demonstrations according to specific models of behavior. However, humans often indicate their goals through a wide range of behaviors, from actions…

Machine Learning · Computer Science 2025-02-26 Will Schwarzer , Jordan Schneider , Philip S. Thomas , Scott Niekum

How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and…

Machine Learning · Statistics 2021-07-08 Mihaela Rosca , Theophane Weber , Arthur Gretton , Shakir Mohamed

Neural Architecture Search (NAS) methods have been shown to outperform hand-designed models and help to democratize AI. However, NAS methods often start from scratch with each new task, making them computationally expensive and limiting…

Machine Learning · Computer Science 2025-07-15 Prabhant Singh , Joaquin Vanschoren

Remote sensing provides satellite data in diverse types and formats. The usage of multimodal learning networks exploits this diversity to improve model performance, except that the complexity of such networks comes at the expense of their…

Machine Learning · Computer Science 2025-08-12 Hiba Najjar , Bushra Alshbib , Andreas Dengel

In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be…

Computer Vision and Pattern Recognition · Computer Science 2019-03-08 Ronghang Hu , Jacob Andreas , Trevor Darrell , Kate Saenko

Subset selection tasks, arise in recommendation systems and search engines and ask to select a subset of items that maximize the value for the user. The values of subsets often display diminishing returns, and hence, submodular functions…

Machine Learning · Computer Science 2023-05-05 Anay Mehrotra , Nisheeth K. Vishnoi

Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how different architectures may lead to inductive bias when trained…

Machine Learning · Computer Science 2024-03-27 Cédric Gerbelot , Avetik Karagulyan , Stefani Karp , Kavya Ravichandran , Menachem Stern , Nathan Srebro

Dataset bias and spurious correlations can significantly impair generalization in deep neural networks. Many prior efforts have addressed this problem using either alternative loss functions or sampling strategies that focus on rare…

Machine Learning · Computer Science 2024-04-16 Robik Shrestha , Kushal Kafle , Christopher Kanan