6 citations · 10 across the 8 of their papers we have counts for
9 papers
Evaluating Pretrained models for Deployable Lifelong Learning
Kiran Lekkala, Eshan Bhargava, Yunhao Ge +1
We create a novel benchmark for evaluating a Deployable Lifelong Learning system for Visual Reinforcement Learning (RL) that is pretrained on a curated dataset, and propose a novel…
Value Explicit Pretraining for Learning Transferable Representations
Kiran Lekkala, Henghui Bao, Sumedh A. Sontakke +2
Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose \textit{Value Explicit Pretraining}…
Bird's Eye View Based Pretrained World model for Visual Navigation
Kiran Lekkala, Chen Liu, Laurent Itti
Sim2Real transfer has gained popularity because it helps transfer from inexpensive simulators to real world. This paper presents a novel system that fuses components in a tradition…
Lightweight Learner for Shared Knowledge Lifelong Learning
Yunhao Ge, Yuecheng Li, Di Wu +12
In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicat…
Ferroelectric FET based Context-Switching FPGA Enabling Dynamic Reconfiguration for Adaptive Deep Learning Machines
Yixin Xu, Zijian Zhao, Yi Xiao +16
Field Programmable Gate Array (FPGA) is widely used in acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, c…
What can we learn from misclassified ImageNet images?
Shixian Wen, Amanda Sofie Rios, Kiran Lekkala +1
Understanding the patterns of misclassified ImageNet images is particularly important, as it could guide us to design deep neural networks (DNN) that generalize better. However, th…
Shaped Policy Search for Evolutionary Strategies using Waypoints
Kiran Lekkala, Laurent Itti
In this paper, we try to improve exploration in Blackbox methods, particularly Evolution strategies (ES), when applied to Reinforcement Learning (RL) problems where intermediate wa…
Attentive Feature Reuse for Multi Task Meta learning
Kiran Lekkala, Laurent Itti
We develop new algorithms for simultaneous learning of multiple tasks (e.g., image classification, depth estimation), and for adapting to unseen task/domain distributions within th…
Meta Adaptation using Importance Weighted Demonstrations
Kiran Lekkala, Sami Abu-El-Haija, Laurent Itti
Imitation learning has gained immense popularity because of its high sample-efficiency. However, in real-world scenarios, where the trajectory distribution of most of the tasks dyn…