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Data augmentation is vital for object detection tasks that require expensive bounding box annotations. Recent successes in diffusion models have inspired the use of diffusionbased synthetic images for data augmentation. However, existing works have primarily focused on image classification, and their applicability to boost object detection’s performance remains unclear. To address this gap, we propose a

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage

Machine Learning Engineer

Sr. Machine Learning Engineer

To build scalable models for the challenging real-world tasks, it is important to learn from diverse, multi-modal data in various forms (e.g., videos, text, images). Amongst the existing works, a plethora of them have been focusing on leveraging large but cumbersome cross-modal architectures. Regardless of their effectiveness, larger architectures unavoidably prevent the models from being extended to realworld

The Net Promoter Score (NPS) is often used in customer experience programs for measuring customer loyalty. Increasingly more companies seek to automatically process millions of pieces of customer feedback from social media per month in order to estimate their NPS, leveraging advanced analytics like machine learning (ML) and natural language processing (NLP). Discovering trends and themes in customer interactions

The recent surge in Large Language Model (LLM) related applications has led to a concurrent escalation in expectations for LLMs to accommodate a myriad of personas and encompass a broad spectrum of perspectives. An important first step towards addressing this demand is to align language models with specific personas, be it groups of users or individuals. Towards this goal, we first present a new conceptualization

Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive personal data. Existing approaches for fingerprint generation are limited in their ability to generate diverse impressions of the same finger, a key property for providing effective data for training recognition models. To address this

The asymmetrical retrieval setting is a well suited solution for resource constrained applications such as face recognition and image retrieval. In this setting, a large model is used for indexing the gallery while a lightweight model is used for querying. The key principle in such systems is ensuring that both models share the same embedding space. Most methods in this domain are based on knowledge distillation

New IAM Access Analyzer feature uses automated reasoning to ensure that access policies written in the IAM policy language don’t inadvertently grant illicit access.

Hierarchies are common structures used to organize data, such as e-commerce hierarchies associated with product data. With these product hierarchies, we aim to learn hierarchy-aware product text embeddings to improve fine-tuning performance on a variety of downstream e-commerce tasks. Existing methods leverage hierarchies by either aligning the text embeddings to separate hierarchical embeddings or by aligning

Large-scale pre-trained vision-language models (VLM) such as CLIP [32] have demonstrated noteworthy zero-shot classification capability, achieving 76.3% top-1 accuracy on ImageNet without seeing any examples. However, while applying CLIP to a downstream target domain, the presence of visual and text domain gaps and cross-modality misalignment can greatly impact the model performance. To address such challenges

Senior Software Dev Engineer

Recent works have shown great success in synchronizing lip-movements in a given video with a dubbed audio stream. However, comparison and efficacy of the synchronization capabilities of these methods is still weakly substantiated due to the lack of a generalized and visually-grounded evaluation method. This work proposes a simple and grounded algorithm – PhoVis, that can measure synchronization and the

Robust, low-loss photonic packaging of on-chip nanophotonic circuits is a key enabling technology for the deployment of integrated photon- ics in a variety of classical and quantum technologies including optical communications and quantum communications, sensing, and trans- duction. To date, no process has been established that enables permanent, broadband, and cryogenically compatible coupling with sub-dB

Using gradient diversity to optimize selection of past samples for retention improves performance while combatting catastrophic forgetting.