22 research outputs found
Renormalized Graph Neural Networks
Graph Neural Networks (GNNs) have become essential for studying complex data,
particularly when represented as graphs. Their value is underpinned by their
ability to reflect the intricacies of numerous areas, ranging from social to
biological networks. GNNs can grapple with non-linear behaviors, emerging
patterns, and complex connections; these are also typical characteristics of
complex systems. The renormalization group (RG) theory has emerged as the
language for studying complex systems. It is recognized as the preferred lens
through which to study complex systems, offering a framework that can untangle
their intricate dynamics. Despite the clear benefits of integrating RG theory
with GNNs, no existing methods have ventured into this promising territory.
This paper proposes a new approach that applies RG theory to devise a novel
graph rewiring to improve GNNs' performance on graph-related tasks. We support
our proposal with extensive experiments on standard benchmarks and baselines.
The results demonstrate the effectiveness of our method and its potential to
remedy the current limitations of GNNs. Finally, this paper marks the beginning
of a new research direction. This path combines the theoretical foundations of
RG, the magnifying glass of complex systems, with the structural capabilities
of GNNs. By doing so, we aim to enhance the potential of GNNs in modeling and
unraveling the complexities inherent in diverse systems
Multimodal Neural Databases
The rise in loosely-structured data available through text, images, and other
modalities has called for new ways of querying them. Multimedia Information
Retrieval has filled this gap and has witnessed exciting progress in recent
years. Tasks such as search and retrieval of extensive multimedia archives have
undergone massive performance improvements, driven to a large extent by recent
developments in multimodal deep learning. However, methods in this field remain
limited in the kinds of queries they support and, in particular, their
inability to answer database-like queries. For this reason, inspired by recent
work on neural databases, we propose a new framework, which we name Multimodal
Neural Databases (MMNDBs). MMNDBs can answer complex database-like queries that
involve reasoning over different input modalities, such as text and images, at
scale. In this paper, we present the first architecture able to fulfill this
set of requirements and test it with several baselines, showing the limitations
of currently available models. The results show the potential of these new
techniques to process unstructured data coming from different modalities,
paving the way for future research in the area. Code to replicate the
experiments will be released at
https://github.com/GiovanniTRA/MultimodalNeuralDatabase
Shape Registration in the Time of Transformers
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the transformers architecture in the registration task. Our method is general and applies to different settings. Given a fixed template with some desired properties (e.g. skinning weights or other animation cues), we can register raw acquired data to it, thereby transferring all the template properties to the input geometry. Alternatively, given a pair of shapes, our method can register the first onto the second (or vice-versa), obtaining a high-quality dense correspondence between the two. In both contexts, the quality of our results enables us to target real applications such as texture transfer and shape interpolation. Furthermore, we also show that including an estimation of the underlying density of the surface eases the learning process. By exploiting the potential of this architecture, we can train our model requiring only a sparse set of ground truth correspondences (10∼20% of the total points). The proposed model and the analysis that we perform pave the way for future exploration of transformer-based architectures for registration and matching applications. Qualitative and quantitative evaluations demonstrate that our pipeline outperforms state-of-the-art methods for deformable and unordered 3D data registration on different datasets and scenarios
Sparse Vicious Attacks on Graph Neural Networks
Graph Neural Networks (GNNs) have proven to be successful in several
predictive modeling tasks for graph-structured data.
Amongst those tasks, link prediction is one of the fundamental problems for
many real-world applications, such as recommender systems.
However, GNNs are not immune to adversarial attacks, i.e., carefully crafted
malicious examples that are designed to fool the predictive model.
In this work, we focus on a specific, white-box attack to GNN-based link
prediction models, where a malicious node aims to appear in the list of
recommended nodes for a given target victim.
To achieve this goal, the attacker node may also count on the cooperation of
other existing peers that it directly controls, namely on the ability to inject
a number of ``vicious'' nodes in the network.
Specifically, all these malicious nodes can add new edges or remove existing
ones, thereby perturbing the original graph.
Thus, we propose SAVAGE, a novel framework and a method to mount this type of
link prediction attacks.
SAVAGE formulates the adversary's goal as an optimization task, striking the
balance between the effectiveness of the attack and the sparsity of malicious
resources required.
Extensive experiments conducted on real-world and synthetic datasets
demonstrate that adversarial attacks implemented through SAVAGE indeed achieve
high attack success rate yet using a small amount of vicious nodes.
Finally, despite those attacks require full knowledge of the target model, we
show that they are successfully transferable to other black-box methods for
link prediction
RRAML: Reinforced Retrieval Augmented Machine Learning
The emergence of large language models (LLMs) has revolutionized machine
learning and related fields, showcasing remarkable abilities in comprehending,
generating, and manipulating human language. However, their conventional usage
through API-based text prompt submissions imposes certain limitations in terms
of context constraints and external source availability. To address these
challenges, we propose a novel framework called Reinforced Retrieval Augmented
Machine Learning (RRAML). RRAML integrates the reasoning capabilities of LLMs
with supporting information retrieved by a purpose-built retriever from a vast
user-provided database. By leveraging recent advancements in reinforcement
learning, our method effectively addresses several critical challenges.
Firstly, it circumvents the need for accessing LLM gradients. Secondly, our
method alleviates the burden of retraining LLMs for specific tasks, as it is
often impractical or impossible due to restricted access to the model and the
computational intensity involved. Additionally we seamlessly link the
retriever's task with the reasoner, mitigating hallucinations and reducing
irrelevant, and potentially damaging retrieved documents. We believe that the
research agenda outlined in this paper has the potential to profoundly impact
the field of AI, democratizing access to and utilization of LLMs for a wide
range of entities
INCB84344-201: Ponatinib and steroids in frontline therapy for unfit patients with Ph+ acute lymphoblastic leukemia
Tyrosine kinase inhibitors have improved survival for patients with Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL). However, prognosis for old or unfit patients remains poor. In the INCB84344-201 (formerly GIMEMA LAL 1811) prospective, multicenter, phase 2 trial, we tested the efficacy and safety of ponatinib plus prednisone in newly diagnosed patients with Ph+ ALL 6560 years, or unfit for intensive chemotherapy and stem cell transplantation. Forty-four patients received oral ponatinib 45 mg/d for 48 weeks (core phase), with prednisone tapered to 60 mg/m2/d from days-14-29. Prophylactic intrathecal chemotherapy was administered monthly. Median age was 66.5 years (range, 26-85). The primary endpoint (complete hematologic response [CHR] at 24 weeks) was reached in 38/44 patients (86.4%); complete molecular response (CMR) in 18/44 patients (40.9%) at 24 weeks. 61.4% of patients completed the core phase. As of 24 April 2020, median event-free survival was 14.31 months (95% CI 9.30-22.31). Median overall survival and duration of CHR were not reached; median duration of CMR was 11.6 months. Most common treatment-emergent adverse events (TEAEs) were rash (36.4%), asthenia (22.7%), alanine transaminase increase (15.9%), erythema (15.9%), and \u3b3-glutamyltransferase increase (15.9%). Cardiac and vascular TEAEs occurred in 29.5% (grade 653, 18.2%) and 27.3% (grade 653, 15.9%), respectively. Dose reductions, interruptions, and discontinuations due to TEAEs occurred in 43.2%, 43.2%, and 27.3% of patients, respectively; 5 patients had fatal TEAEs. Ponatinib and prednisone showed efficacy in unfit patients with Ph+ ALL; however, a lower ponatinib dose may be more appropriate in this population. This trial was registered at www.clinicaltrials.gov as #NCT01641107
Multiresolution topological data analysis for robust activity tracking
Multidimensional sensors represent an increasingly popular, yet challenging data source in modern statistics. Using tools from the emerging branch of Topological Data Analysis (TDA), we address two issues frequently encountered when analysing sensor data, namely their (often) high dimension and their sensibility to the reference system. We show how topological invariants provide a tool for detecting change--points which is robust with respect to both the time resolution we consider and the sensor placement
CycleDRUMS: automatic drum arrangement for bass lines using CycleGAN
Abstract The two main research threads in computer-based music generation are the construction of autonomous music-making systems and the design of computer-based environments to assist musicians. In the symbolic domain, the key problem of automatically arranging a piece of music was extensively studied, while relatively fewer systems tackled this challenge in the audio domain. In this contribution, we propose CycleDRUMS, a novel method for generating drums given a bass line. After converting the waveform of the bass into a mel-spectrogram, we can automatically generate original drums that follow the beat, sound credible, and be directly mixed with the input bass. We formulated this task as an unpaired image-to-image translation problem, and we addressed it with CycleGAN, a well-established unsupervised style transfer framework designed initially for treating images. The choice to deploy raw audio and mel-spectrograms enabled us to represent better how humans perceive music and to draw sounds for new arrangements from the vast collection of music recordings accumulated in the last century. In the absence of an objective way of evaluating the output of both generative adversarial networks and generative music systems, we further defined a possible metric for the proposed task, partially based on human (and expert) judgment. Finally, as a comparison, we replicated our results with Pix2Pix, a paired image-to-image translation network, and we showed that our approach outperforms it