21 papers · ranked by Valyu relevance
Alok Sharma, Artem Lysenko, Shangru Jia, Keith A. Boroevich + 1 more
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques…
Siyi Li, Chunyu Sun, Jiahao Zhang, Yuchen Kang + 5 more
Evaluating a Physical AI stack spans operators that differ by more than three orders of magnitude -- from a single foundation-model decoding step to thousands of physics ticks of whole-body control -- varying orthogonally in modality, reward semantics, and resource profile. No existing framework spans this range, so…
Alok Sharma, Yosvany López, Shangru Jia, Artem Lysenko + 2 more
'Keith A. Boroevich' 'Tatsuhiko Tsunoda'] Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the…
Shangru Jia, Artem Lysenko, Keith A. Boroevich, Alok Sharma + 1 more
DeepInsight-style methods make tabular feature relationships accessible to convolutional networks by placing each feature at a fixed position on an image carrier. An open design question is how the carrier geometry should be constructed when feature neighborhoods themselves carry part of the signal. We introduce…
Tansel Ersavas, Martin A. Smith, John S. Mattick
Convolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial Intelligence. However, in recent years newer architectures such as Transformers have dominated both research and practical applications. While CNNs still play critical…
Alok Sharma, Yosvany López, Shangru Jia, Artem Lysenko + 2 more
Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods have been employed for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies…
Alok Sharma, Artem Lysenko, Keith A Boroevich, Tatsuhiko Tsunoda
Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcomes. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging…
Elif İlgazi Kılıç, Şafak Kılıç
Background Cervical cancer remains one of the leading causes of gynecological mortality worldwide, largely due to the limitations of manual cytological screening, which is time-consuming and susceptible to inter-observer variability. Although deep learning has demonstrated strong potential for automating cervical…
Kengo Inutsuka, Tadaaki Nishioka, Tom Macpherson, Mana Fujiwara + 2 more
When and how does insight emerge? We conceptualize insight as a sudden realization arising from restructuring a world model: an internal interpretation linking actions to outcomes. Yet these latent dynamics remain difficult to access, even with behavior and verbal report. Here we developed inside insight dynamics…
Authors not listed
The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…
Zhijie Wang, Yuheng Huang, Da Song, Lei Ma + 1 more
To evaluate user performance on model understanding, two authors manually assessed and coded participants' responses and counted the number of correct insights about model behavior shared by participants. Specifically, these two authors had 4 meetings to develop a codebook and resolve labeling inconsistencies.…
Misako Kimura, Yuuki Matsushita, Masayo Inoue, Shigeto Seno + 5 more
Insight is often described as a sudden shift in or formation of a conceptual representation, enabling humans to restructure existing knowledge and solve problems beyond conventional analytical approaches. Although prior computational studies have modeled aspects of insight using deep neural networks (DNNs) or…
HOSSEIN MORADIMOKHLES, GWO-JEN HWANG, HOSSEIN ZANGENEH, MARYAM POURJAMSHIDI + 1 more
The term “deep learning” has incorrect interpretations in education and technology disciplines. It describes a method of learning in which the objective is to achieve an in-depth understanding of topic rather than succumb to surface learning. "Non-surface learning" is undeniably related to deep information…
Sofiia Chorna, Kateryna Tarelkina, Eloïse Berthier, Gianni Franchi
While concept-based interpretability methods have traditionally focused on local explanations of neural network predictions, we propose a novel framework and interactive tool that extends these methods into the domain of mechanistic interpretability. Our approach enables a global dissection of model behavior by…
Tommaso Calò, Luigi De Russis
Deep Learning (DL) developers come from different backgrounds, e.g., medicine, genomics, finance, and computer science. To create a DL model, they must learn and use high-level programming languages (e.g., Python), thus needing to handle related setups and solve programming errors. This paper presents DeepBlocks, a…
Dennis Gankin, Pedro Beltrao
Biologically inspired neural networks (BINNs) embed pathway, ontology, or protein-interaction structure directly into neural networks, promising interpretable disease prediction where hidden nodes map to named biological entities. Yet BINNs have been hard to train at biobank scale, and the reliability of their…
Mohit Prabhushankar, Ghassan AlRegib
| Citation | M. Prabhushankar and G AlRegib, "Introspective Learning : A Two-Stage Approach | | --- | --- | | | for Inference in Neural Networks" Advances in Neural Information Processing | | | Systems (2022), Nov 29 - Dec 1, 2022. | | Review | Data of Submission : 19 May 2022 | | | Date of Revision : 2 Aug 2022 | | |…
Farzan Shenavarmasouleh, Farid Ghareh Mohammadi, Khaled Rasheed, Hamid R. Arabnia
'Hamid R. Arabnia'] Abstract— Deep learning (DL) along with never-ending advancements in computational processing and cloud technologies have bestowed us powerful analyzing tools and techniques in the past decade and enabled us to use and apply them in various fields of study. Health informatics is not an exception…
Anupam Ojha, Saumya Thakur, Surl-Hee Ahn, Rommie Amaro
Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this…
Authors not listed
Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core-shell upconverting nanoparticles (UCNPs). UCNPs, which have…
Benjamin Hoar, Weitong Zhang, Shuangning Xu, Rana Deeba + 3 more
For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous…