Search Results for author:
Found 10 papers, 2 papers with code
CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers
Contrastive Language-Image Pre-training (CLIP) has been shown to improve zero-shot generalization capabilities of language and vision models.
Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection
Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware.
A Blueprint for Precise and Fault-Tolerant Analog Neural Networks
Our study demonstrates that analog accelerators utilizing the RNS-based approach can achieve ${\geq}99\%$ of FP32 accuracy for state-of-the-art DNN inference using data converters with only $6$-bit precision whereas a conventional analog core requires more than $8$-bit precision to achieve the same accuracy in the same DNNs.
INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers
To supplement this ongoing effort, we propose INT-FP-QSim: an open-source simulator that enables flexible evaluation of LLMs and vision transformers at various numerical precisions and formats.
Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware
Existing methods to recover model accuracy on analog-digital hardware in the presence of quantization and analog noise include noise-injection training.
Adaptive Block Floating-Point for Analog Deep Learning Hardware
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts.
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems.
Towards Robust One-shot Task Execution using Knowledge Graph Embeddings
We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
Tool Substitution with Shape and Material Reasoning Using Dual Neural Networks
This paper explores the problem of tool substitution, namely, identifying substitute tools for performing a task from a given set of candidate tools.
Action Categorization for Computationally Improved Task Learning and Planning
This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL).
