11 citations · 24 across the 26 of their papers we have counts for
6 papers · 1 filter
SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction
Dan Jacobellis, Neeraja J. Yadwadkar
In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and on-device compute resources p…
FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder
Dan Jacobellis, Neeraja J. Yadwadkar
Media compression standards have reached a plateau in terms of the rate-distortion-complexity trade-off, limiting the ability to offload expensive AI perception to the cloud in app…
LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation
Dan Jacobellis, Neeraja J. Yadwadkar
Modern sensors generate rich, high-fidelity data, yet applications operating on wearable or remote sensing devices remain constrained by bandwidth and power budgets. Standardized c…
DeDelayed: Deleting Remote Inference Delay via On-Device Correction
Dan Jacobellis, Mateen Ulhaq, Fabien Racapé +2
Video comprises the vast majority of bits that are generated daily, and is the primary signal driving current innovations in robotics, remote sensing, and wearable technology. Yet,…
Learned Compression for Compressed Learning
Dan Jacobellis, Neeraja J. Yadwadkar
Modern sensors produce increasingly rich streams of high-resolution data. Due to resource constraints, machine learning systems discard the vast majority of this information via re…
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image Models
Dan Jacobellis, Daniel Cummings, Neeraja J. Yadwadkar
In the field of neural data compression, the prevailing focus has been on optimizing algorithms for either classical distortion metrics, such as PSNR or SSIM, or human perceptual q…