30 citations · 56 across the 8 of their papers we have counts for
8 papers
Efficient LLM Moderation with Multi-Layer Latent Prototypes
Maciej Chrabąszcz, Filip Szatkowski, Bartosz Wójcik +3
Although modern LLMs are aligned with human values during post-training, robust moderation remains essential to prevent harmful outputs at deployment time. Existing approaches suff…
How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies
Piotr Kubaty, Bartosz Wójcik, Bartłomiej Krzepkowski +4
Early exits enable the network's forward pass to terminate early by attaching trainable internal classifiers to the backbone network. Existing early-exit methods typically adopt ei…
Hypernetworks build Implicit Neural Representations of Sounds
Filip Szatkowski, Karol J. Piczak, Przemysław Spurek +2
Implicit Neural Representations (INRs) are nowadays used to represent multimedia signals across various real-life applications, including image super-resolution, image compression,…
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Filip Szatkowski, Karol J. Piczak, Przemysław Spurek +2
Implicit neural representations (INRs) are a rapidly growing research field, which provides alternative ways to represent multimedia signals. Recent applications of INRs include im…
Continual Learning with Guarantees via Weight Interval Constraints
Maciej Wołczyk, Karol J. Piczak, Bartosz Wójcik +5
We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting. Contemporary Continual Learning (CL) methods focus…
HyperCube: Implicit Field Representations of Voxelized 3D Models
Magdalena Proszewska, Marcin Mazur, Tomasz Trzciński +1
Recently introduced implicit field representations offer an effective way of generating 3D object shapes. They leverage implicit decoder trained to take a 3D point coordinate conca…
Efficient GPU implementation of randomized SVD and its applications
Łukasz Struski, Paweł Morkisz, Przemysław Spurek +2
Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for m…
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy +4
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by att…