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Mitigating Domain Shift via Self-training in Single and Multi-target Unsupervised Domain Adaptation
Though deep learning has achieved significant successes in many computer vision tasks, the state-of-the-art approaches rely on the availability of a large amount of labeled data for supervision, collection of which is ...
Improving photoacoustic imaging with model compensating and deep learning methods
Photoacoustic imaging is a hybrid biomedical imaging technique combining optical ab-
sorption contrast with ultrasonic resolution. It is a non-invasive technique that is scalable
to reveal structural, functional, and ...
Coarse-grained dynamics derived structural ensemble for prediction of metal binding sites of protein and phenotypic effects of variants
Structures of proteins play a key role in determining their functions. Knowledge of structure,
especially the details of specific sites of a protein can help us understand their contribution to
the overall activity. ...
Learning Across Domains: Applications to Text-based Person Search and Multi-Source Domain Adaptation
With rapid development in technology and ubiquitous presence of diverse types of sensors, a large
amount of data from different modalities (e.g., text, audio, images etc.) describing the same person/
object/event has ...
Numerical Analysis of Some Preconditioners and Associated Error Estimators for Solving Linear Systems
Convergence of iterative algorithms in solving large linear systems is largely affected by the condition number of the matrix. Preconditioners reduce the condition number of the system matrix, thereby letting the linear ...
Towards Learning Adversarially Robust Deep Learning Models
Deep learning models have shown impressive performance across a wide spectrum of computer vision
applications, including medical diagnosis and autonomous driving. One of the major concerns that
these models face is their ...
Scalability Bottleneck Analysis of High Performance Applications
Obtaining high performance and scalability for high performance applications are challenging.
There are various bottlenecks including, higher rate of memory access, complex algorithm, high
rate of communication, big ...
Improving Data Center Utilisation by Reducing Fragmentation
Virtualization enables better server consolidation and utilisation compared to stand-alone
servers running a single workload. This enabled wide-spread cloud adoption among many
organizations. Data center utilisation is ...
A Divide and Conquer Framework For Graph Processing in Distributed Heterogeneous Systems
In many fields of science and engineering graph data structures are used to represent real-world
information. As these graphs scale in size, it becomes very inefficient to process these graphs on
a single core CPU using ...
EMF: System Design and Challenges for Disaggregated GPUs in datacenters for Efficiency, Modularity and Flexibility
With Dennard Scaling phasing out in the mid-2000s, architectural scaling and hardware specialization
take centre stage to provide performance bene fits with already stalling Moore's law. An
outcome from this hardware ...