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Found 22 papers, 6 papers with code
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification
RadioTransformer fills this critical gap by learning from radiologists' visual search patterns, encoded as 'human visual attention regions' in a cascaded global-focal transformer framework.
Vi-Fi: Associating Moving Subjects across Vision and Wireless Sensors
In this paper, we present Vi-Fi, a multi-modal system that leverages a user’s smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e. g. WiFi MAC address).
ViTag: Online WiFi Fine Time Measurements Aided Vision-Motion Identity Association in Multi-person Environments
ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Measurement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones.
ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements
Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle to pedestrian communication and so on.
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
FATA-Trans is field- and time-aware for sequential tabular data.
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
SparCE consists of 2 key micro-architectural enhancements- a Sparsity Register File (SpRF) that tracks zero registers and a Sparsity aware Skip Address (SASA) table that indicates instructions to be skipped.
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious.
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN).
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems.
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs).
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
To address this challenge, there is a need for tools that can model the functional impact of non-idealities on DNN training and inference.
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration
To efficiently support precision re-configurability in DNN accelerators, we introduce an approximate computing method wherein DNN computations are performed block-wise (a block is a group of bits) and re-configurability is supported at the granularity of blocks.
Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework
With these metrics, one can easily identify meta-features with the most complementary behaviors in two classifiers, and use them to better ensemble the classifiers.
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning
We introduce ViFiCon, a self-supervised contrastive learning scheme which uses synchronized information across vision and wireless modalities to perform cross-modal association.
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.
Data-Side Efficiencies for Lightweight Convolutional Neural Networks
We examine how the choice of data-side attributes for two important visual tasks of image classification and object detection can aid in the choice or design of lightweight convolutional neural networks.
Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models
First, we propose a procedure for the development and evaluation of document-level membership inference for LLMs by leveraging commonly used data sources for training and the model release date.
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets.
Feudal Networks for Visual Navigation
We introduce a new approach to visual navigation using feudal learning, which employs a hierarchical structure consisting of a worker agent, a mid-level manager, and a high-level manager.
A Landmark-Aware Visual Navigation Dataset
However, recent advancements in the visual navigation field face challenges due to the lack of human datasets in the real world for efficient supervised representation learning of the environments.
A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics
We show how this measure can help a practitioner select a computationally efficient model for a small dataset 6 to 29x faster than through repeated training and testing.
