Zobrazeno 1 - 10
of 2 591
pro vyhledávání: '"Bajic, P"'
Autor:
Hadizadeh, Hadi, Bajić, Ivan V.
Autonomous driving sensors generate an enormous amount of data. In this paper, we explore learned multimodal compression for autonomous driving, specifically targeted at 3D object detection. We focus on camera and LiDAR modalities and explore several
Externí odkaz:
http://arxiv.org/abs/2408.08211
Thermostatically-controlled loads have a significant impact on electricity demand after service is restored following an outage, a phenomenon known as cold load pick-up (CLPU). Active management of CLPU is becoming an essential tool for distribution
Externí odkaz:
http://arxiv.org/abs/2408.07754
Autor:
Ulhaq, Mateen, Bajić, Ivan V.
The entropy bottleneck introduced by Ball\'e et al. is a common component used in many learned compression models. It encodes a transformed latent representation using a static distribution whose parameters are learned during training. However, the a
Externí odkaz:
http://arxiv.org/abs/2406.13059
Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machine learning. SplitFed aims to minimize the computational burden on indi
Externí odkaz:
http://arxiv.org/abs/2405.19453
In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal datasets and the rapid progress in multimodal learning systems. Well-known
Externí odkaz:
http://arxiv.org/abs/2405.12456
Autor:
de Andrade, Anderson, Bajić, Ivan
We identify an issue in multi-task learnable compression, in which a representation learned for one task does not positively contribute to the rate-distortion performance of a different task as much as expected, given the estimated amount of informat
Externí odkaz:
http://arxiv.org/abs/2405.10244
Autor:
Alvar, Saeed Ranjbar, Bajić, Ivan V.
Deep models produce a number of features in each internal layer. A key problem in applications such as feature compression for remote inference is determining how important each feature is for the task(s) performed by the model. The problem is especi
Externí odkaz:
http://arxiv.org/abs/2405.09077
Autor:
Hasselmann, Ken, Malizia, Mario, Caballero, Rafael, Polisano, Fabio, Govindaraj, Shashank, Stigler, Jakob, Ilchenko, Oleksii, Bajic, Milan, De Cubber, Geert
Publikováno v:
IEEE ICRA Workshop on Field Robotics 2024
In order to clear the world of the threat posed by landmines and other explosive devices, robotic systems can play an important role. However, the development of such field robots that need to operate in hazardous conditions requires the careful cons
Externí odkaz:
http://arxiv.org/abs/2404.14167
For modeling the number of visits in Stopi\'{c}a cave (Serbia) we consider the classical Auto-regressive Integrated Moving Average (ARIMA) model, Machine Learning (ML) method Support Vector Regression (SVR), and hybrid NeuralPropeth method which comb
Externí odkaz:
http://arxiv.org/abs/2404.04974
In the wood industry, logs are commonly quality screened by discrete X-ray scans on a moving conveyor belt from a few source positions. Typically, two-dimensional (2D) slice-wise measurements are obtained by a sequential scanning geometry. Each 2D sl
Externí odkaz:
http://arxiv.org/abs/2403.02820