Zobrazeno 1 - 10
of 178
pro vyhledávání: '"Srinivasan, Ashwin"'
Autor:
Brahmavar, Shreyas Bhat, Rajesh, Rohit, Dash, Tirtharaj, Vig, Lovekesh, Verlekar, Tanmay Tulsidas, Hasan, Md Mahmudul, Khan, Tariq, Meijering, Erik, Srinivasan, Ashwin
Publikováno v:
IEEE International Conference on Image Processing (ICIP 2023)
Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%. However, the following aspects remain unaddressed: State-of-the-art models are complex and require substantial computational infrastructure
Externí odkaz:
http://arxiv.org/abs/2303.02310
Autor:
Chitnis, Soham Rohit, Liu, Sidong, Dash, Tirtharaj, Verlekar, Tanmay Tulsidas, Di Ieva, Antonio, Berkovsky, Shlomo, Vig, Lovekesh, Srinivasan, Ashwin
Publikováno v:
Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2023)
Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in comp
Externí odkaz:
http://arxiv.org/abs/2302.09833
We model short-duration (e.g. day) trading in financial markets as a sequential decision-making problem under uncertainty, with the added complication of continual concept-drift. We, therefore, employ meta reinforcement learning via the RL2 algorithm
Externí odkaz:
http://arxiv.org/abs/2302.08996
Recent engineering developments have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical roots in the design of autonomous agents. However, relatively little attention ha
Externí odkaz:
http://arxiv.org/abs/2301.01819
Autor:
Srinivasan, Ashwin
Machine learning and natural language processing (NLP) can help describe and make inferences on the vast amount of text data in cybersecurity. We use a graph database named BRON, which contains data from publicly available threat and vulnerability so
Externí odkaz:
https://hdl.handle.net/1721.1/143257
Autor:
Shah, Vedant, Agrawal, Aditya, Vig, Lovekesh, Srinivasan, Ashwin, Shroff, Gautam, Verlekar, Tanmay
We are interested in neurosymbolic systems consisting of a high-level symbolic layer for explainable prediction in terms of human-intelligible concepts; and a low-level neural layer for extracting symbols required to generate the symbolic explanation
Externí odkaz:
http://arxiv.org/abs/2211.16047
Autor:
Hudson, Karina, Srinivasan, Ashwin, Miserev, Dmitry, Wang, Qingwen, Klochan, Oleh, Sushkov, Oleg, Farrer, Ian, Ritchie, David, Hamilton, Alex
Many modern spin-based devices rely on the spin-orbit interaction, which is highly sensitive to the host semiconductor heterostructure and varies substantially depending on crystal direction, crystal asymmetry (Dresselhaus), and quantum confinement a
Externí odkaz:
http://arxiv.org/abs/2211.00253
Autor:
Shah, Vishwa, Sharma, Aditya, Shroff, Gautam, Vig, Lovekesh, Dash, Tirtharaj, Srinivasan, Ashwin
Analogical Reasoning problems challenge both connectionist and symbolic AI systems as these entail a combination of background knowledge, reasoning and pattern recognition. While symbolic systems ingest explicit domain knowledge and perform deductive
Externí odkaz:
http://arxiv.org/abs/2209.08750
Publikováno v:
Mach Learn (2023)
Relational machine learning programs like those developed in Inductive Logic Programming (ILP) offer several advantages: (1) The ability to model complex relationships amongst data instances; (2) The use of domain-specific relations during model cons
Externí odkaz:
http://arxiv.org/abs/2206.00738
Recent engineering developments in specialised computational hardware, data-acquisition and storage technology have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical ro
Externí odkaz:
http://arxiv.org/abs/2205.08954