7 research outputs found
Programmable Agents
We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop disentangled interpretable representations that allow them to
generalize to a wide variety of zero-shot semantic tasks
Multiband and Lossless Compression of Hyperspectral Images
Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images
Language Modeling Is Compression
It has long been established that predictive models can be transformed into
lossless compressors and vice versa. Incidentally, in recent years, the machine
learning community has focused on training increasingly large and powerful
self-supervised (language) models. Since these large language models exhibit
impressive predictive capabilities, they are well-positioned to be strong
compressors. In this work, we advocate for viewing the prediction problem
through the lens of compression and evaluate the compression capabilities of
large (foundation) models. We show that large language models are powerful
general-purpose predictors and that the compression viewpoint provides novel
insights into scaling laws, tokenization, and in-context learning. For example,
Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to
43.4% and LibriSpeech samples to 16.4% of their raw size, beating
domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively.
Finally, we show that the prediction-compression equivalence allows us to use
any compressor (like gzip) to build a conditional generative model
Compression and protection of multidimensional data
2013 - 2014The main objective of this thesis is to explore and discuss novel techniques
related to the compression and protection of multidimensional data (i.e., 3-D
medical images, hyperspectral images, 3-D microscopy images and 5-D
functional Magnetic Resonance Images).
First, we outline a lossless compression scheme based on the predictive
model, denoted as Medical Images Lossless Compression algorithm (MILC).
MILC is characterized to provide a good trade-off between the compression
performances and reduced usage of the hardware resources. Since in the
medical and medical-related fields, the execution speed of an algorithm, could
be a “critical” parameter, we investigate the parallelization of the compression
strategy of the MILC algorithm, which is denoted as Parallel MILC. Parallel
MILC can be executed on heterogeneous devices (i.e., CPUs, GPUs, etc.) and
provides significant results in terms of speedup with respect to the MILC.
This is followed by the important aspects related to the protection of two
sensitive typologies of multidimensional data: 3-D medical images and 3-D
microscopy images. Regarding the protection of 3-D medical images, we outline
a novel hybrid approach, which allows for the efficient compression of 3-D
medical images as well as the embedding of a digital watermark, at the same
time. In relation to the protection of 3-D microscopy images, the simultaneous
embedding of two watermarks is explained. It should be noted that 3-D
microscopy images are often used in delicate tasks (i.e., forensic analysis, etc.).
Subsequently, we review a novel predictive structure that is appropriate for
the lossless compression of different typologies of multidimensional data... [edited by Author]XIII n.s