Qpiai India Private Limited

Aswanth Krishnan

Nagendra Nagaraja

Sachin Kumar

Animesh Tripathy

Lakshya Priyadarshi

Arpit Jain

Hitesh Daksh

A method receiving a dataset including a plurality of images, executing an embedding model using the plurality of images to generate a plurality of image embeddings, clustering the plurality of image embeddings into a plurality of clusters based on similarities between the plurality of image embeddings, determining a density for each cluster based on an aggregate similarity of image embeddings of the cluster, based on the density for each of the plurality of clusters satisfying a criterion, for each cluster: receiving a first set of annotations for one or more pixels of each of a first subset of the images included in the cluster, inputting the annotated first subset of images into a vision transformer with a remaining unannotated subset of images not included in the annotated first subset, and generating a second plurality of annotations for the remaining unannotated subset of images not included in the annotated first subset.

System and method for semi-automated dataset annotation using similarity based clustering and in-context learning for segmentation

Systems and methods for optimizing the placement of experts in a mixture-of-experts (MoE) model are disclosed. A system can include one or more processors coupled to non-transitory memory. The system can obtain a set of attributes of a plurality of hardware components of a distributed computing environment. The system can determine a set of co-activation metrics for a plurality of experts of a mixture-of-experts model according to inference or training operations executed using the mixture-of-experts model. The system can generate a quadratic unconstrained binary optimization (QUBO) data structure based at least on the set of attributes and the set of co-activation metrics. The system can assign the plurality of experts of the mixture-of-experts model to the plurality of hardware components of the distributed computing environment according to the QUBO data structure. The system can execute the mixture-of-experts model using the plurality of hardware components according to the assignment.

Systems and methods for optimizing mixture-of-experts model placement using QUBO-based bin packing

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Bengaluru, India

Aswanth Krishnan