202 research outputs found
Estimating a potential without the agony of the partition function
Estimating a Gibbs density function given a sample is an important problem in
computational statistics and statistical learning. Although the well
established maximum likelihood method is commonly used, it requires the
computation of the partition function (i.e., the normalization of the density).
This function can be easily calculated for simple low-dimensional problems
but its computation is difficult or even intractable for general densities and
high-dimensional problems. In this paper we propose an alternative approach
based on Maximum A-Posteriori (MAP) estimators, we name Maximum Recovery MAP
(MR-MAP), to derive estimators that do not require the computation of the
partition function, and reformulate the problem as an optimization problem. We
further propose a least-action type potential that allows us to quickly solve
the optimization problem as a feed-forward hyperbolic neural network. We
demonstrate the effectiveness of our methods on some standard data sets
DRIP: Deep Regularizers for Inverse Problems
In this paper we consider inverse problems that are mathematically ill-posed.
That is, given some (noisy) data, there is more than one solution that
approximately fits the data. In recent years, deep neural techniques that find
the most appropriate solution, in the sense that it contains a-priori
information, were developed. However, they suffer from several shortcomings.
First, most techniques cannot guarantee that the solution fits the data at
inference. Second, while the derivation of the techniques is inspired by the
existence of a valid scalar regularization function, such techniques do not in
practice rely on such a function, and therefore veer away from classical
variational techniques. In this work we introduce a new family of neural
regularizers for the solution of inverse problems. These regularizers are based
on a variational formulation and are guaranteed to fit the data. We demonstrate
their use on a number of highly ill-posed problems, from image deblurring to
limited angle tomography
pathGCN: Learning General Graph Spatial Operators from Paths
Graph Convolutional Networks (GCNs), similarly to Convolutional Neural
Networks (CNNs), are typically based on two main operations - spatial and
point-wise convolutions. In the context of GCNs, differently from CNNs, a
pre-determined spatial operator based on the graph Laplacian is often chosen,
allowing only the point-wise operations to be learnt. However, learning a
meaningful spatial operator is critical for developing more expressive GCNs for
improved performance. In this paper we propose pathGCN, a novel approach to
learn the spatial operator from random paths on the graph. We analyze the
convergence of our method and its difference from existing GCNs. Furthermore,
we discuss several options of combining our learnt spatial operator with
point-wise convolutions. Our extensive experiments on numerous datasets suggest
that by properly learning both the spatial and point-wise convolutions,
phenomena like over-smoothing can be inherently avoided, and new
state-of-the-art performance is achieved.Comment: ICML 202
An Over Complete Deep Learning Method for Inverse Problems
Obtaining meaningful solutions for inverse problems has been a major
challenge with many applications in science and engineering. Recent machine
learning techniques based on proximal and diffusion-based methods have shown
promising results. However, as we show in this work, they can also face
challenges when applied to some exemplary problems. We show that similar to
previous works on over-complete dictionaries, it is possible to overcome these
shortcomings by embedding the solution into higher dimensions. The novelty of
the work proposed is that we jointly design and learn the embedding and the
regularizer for the embedding vector. We demonstrate the merit of this approach
on several exemplary and common inverse problems
Feature Transportation Improves Graph Neural Networks
Graph neural networks (GNNs) have shown remarkable success in learning
representations for graph-structured data. However, GNNs still face challenges
in modeling complex phenomena that involve feature transportation. In this
paper, we propose a novel GNN architecture inspired by
Advection-Diffusion-Reaction systems, called ADR-GNN. Advection models feature
transportation, while diffusion captures the local smoothing of features, and
reaction represents the non-linear transformation between feature channels. We
provide an analysis of the qualitative behavior of ADR-GNN, that shows the
benefit of combining advection, diffusion, and reaction. To demonstrate its
efficacy, we evaluate ADR-GNN on real-world node classification and
spatio-temporal datasets, and show that it improves or offers competitive
performance compared to state-of-the-art networks.Comment: AAAI 202
Preclinical to clinical development of the novel camptothecin nanopharmaceutical CRLX101
AbstractCamptothecin (CPT) is a potent broad-spectrum anticancer agent that acts through inhibition of topoisomerase 1. Clinical development of CPT was unsuccessful due to poor drug solubility, insufficient in vivo stability of the active form, and toxicity. In order to address these issues, a polymeric nanoparticle comprised of cyclodextrin-poly(ethylene glycol) copolymer (CDP) conjugated to CPT (CRLX101) has been developed and Phase 2 clinical studies are ongoing. Camptothecin is conjugated to the polymer in its active form at 10–12wt.% loading. CRLX101 self-assembles in solution into nanoparticles with an apparent solubility increase of >1000-fold as compared to the parent drug camptothecin. Preclinical studies exhibited CRLX101 pharmacokinetics superior to the parent drug. Drug concentration in tumor relative to plasma and other major organs is consistent with the enhanced permeation and retention (EPR) anticipated from a nanoparticle. Significant anti-tumor activity was observed that is superior when compared to irinotecan across a broad range of xenograft models. Pharmacokinetic data are consistent with the prolonged half-life and increased AUC. The CRLX101 preclinical and clinical data confirm that CDP can address not only solubility, formulation, toxicity, and pharmacokinetic challenges associated with administration of CPT, but more importantly, can impart unique biological properties, that enhance pharmacodynamics and efficacy of camptothecin
CRLX101 (formerly IT-101)–A Novel Nanopharmaceutical of Camptothecin in Clinical Development
CRLX101 (formerly IT-101) is a first-in-class nanopharmaceutical, currently in Phase 2a development, which has been developed by covalently conjugating camptothecin (CPT) to a linear, cyclodextrin-polyethylene glycol (CD-PEG) co-polymer that self-assembles into nanoparticles. As a nanometer-scale drug carrier system, the cyclodextrin polymeric nanoparticle technology, referred to as “CDP”, has unique design features and capabilities. Specifically, CRLX101 preclinical and clinical data confirm that CDP can address not only solubility, formulation, toxicity, and pharmacokinetic challenges associated with administration of CPT, but more importantly, can impart unique biological properties that enhance CPT pharmacodynamics and efficacy
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