1,399 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR)
literature by putting forward a strong baseline that overshoots prior
state-of-the-arts by ~11%. This is not via complicated design though, but by
addressing two critical issues facing the community (i) the gold standard
triplet loss does not enforce holistic latent space geometry, and (ii) there
are never enough sketches to train a high accuracy model. For the former, we
propose a simple modification to the standard triplet loss, that explicitly
enforces separation amongst photos/sketch instances. For the latter, we put
forward a novel knowledge distillation module can leverage photo data for model
training. Both modules are then plugged into a novel plug-n-playable training
paradigm that allows for more stable training. More specifically, for (i) we
employ an intra-modal triplet loss amongst sketches to bring sketches of the
same instance closer from others, and one more amongst photos to push away
different photo instances while bringing closer a structurally augmented
version of the same photo (offering a gain of ~4-6%). To tackle (ii), we first
pre-train a teacher on the large set of unlabelled photos over the
aforementioned intra-modal photo triplet loss. Then we distill the contextual
similarity present amongst the instances in the teacher's embedding space to
that in the student's embedding space, by matching the distribution over
inter-feature distances of respective samples in both embedding spaces
(delivering a further gain of ~4-5%). Apart from outperforming prior arts
significantly, our model also yields satisfactory results on generalising to
new classes. Project page: https://aneeshan95.github.io/Sketch_PVT/Comment: Accepted in CVPR 2023. Project page available at
https://aneeshan95.github.io/Sketch_PVT
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto}
standard in graph representation learning. However, when it comes to link
prediction, they often struggle, surpassed by simple heuristics such as Common
Neighbor (CN). This discrepancy stems from a fundamental limitation: while
MPNNs excel in node-level representation, they stumble with encoding the joint
structural features essential to link prediction, like CN. To bridge this gap,
we posit that, by harnessing the orthogonality of input vectors, pure
message-passing can indeed capture joint structural features. Specifically, we
study the proficiency of MPNNs in approximating CN heuristics. Based on our
findings, we introduce the Message Passing Link Predictor (MPLP), a novel link
prediction model. MPLP taps into quasi-orthogonal vectors to estimate
link-level structural features, all while preserving the node-level
complexities. Moreover, our approach demonstrates that leveraging
message-passing to capture structural features could offset MPNNs'
expressiveness limitations at the expense of estimation variance. We conduct
experiments on benchmark datasets from various domains, where our method
consistently outperforms the baseline methods.Comment: preprin
Exploring the Influence of Information Entropy Change in Learning Systems
In this work, we explore the influence of entropy change in deep learning
systems by adding noise to the inputs/latent features. The applications in this
paper focus on deep learning tasks within computer vision, but the proposed
theory can be further applied to other fields. Noise is conventionally viewed
as a harmful perturbation in various deep learning architectures, such as
convolutional neural networks (CNNs) and vision transformers (ViTs), as well as
different learning tasks like image classification and transfer learning.
However, this paper aims to rethink whether the conventional proposition always
holds. We demonstrate that specific noise can boost the performance of various
deep architectures under certain conditions. We theoretically prove the
enhancement gained from positive noise by reducing the task complexity defined
by information entropy and experimentally show the significant performance gain
in large image datasets, such as the ImageNet. Herein, we use the information
entropy to define the complexity of the task. We categorize the noise into two
types, positive noise (PN) and harmful noise (HN), based on whether the noise
can help reduce the complexity of the task. Extensive experiments of CNNs and
ViTs have shown performance improvements by proactively injecting positive
noise, where we achieved an unprecedented top 1 accuracy of over 95% on
ImageNet. Both theoretical analysis and empirical evidence have confirmed that
the presence of positive noise can benefit the learning process, while the
traditionally perceived harmful noise indeed impairs deep learning models. The
different roles of noise offer new explanations for deep models on specific
tasks and provide a new paradigm for improving model performance. Moreover, it
reminds us that we can influence the performance of learning systems via
information entropy change.Comment: Information Entropy, CNN, Transforme
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Missing Data Imputation with Graph Laplacian Pyramid Network
Data imputation is a prevalent and important task due to the ubiquitousness
of missing data. Many efforts try to first draft a completed data and second
refine to derive the imputation results, or "draft-then-refine" for short. In
this work, we analyze this widespread practice from the perspective of
Dirichlet energy. We find that a rudimentary "draft" imputation will decrease
the Dirichlet energy, thus an energy-maintenance "refine" step is in need to
recover the overall energy. Since existing "refine" methods such as Graph
Convolutional Network (GCN) tend to cause further energy decline, in this work,
we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to
preserve Dirichlet energy and improve imputation performance. GLPN consists of
a U-shaped autoencoder and residual networks to capture global and local
detailed information respectively. By extensive experiments on several
real-world datasets, GLPN shows superior performance over state-of-the-art
methods under three different missing mechanisms. Our source code is available
at https://github.com/liguanlue/GLPN.Comment: 12 pages, 5 figure
Perceptions and Practicalities for Private Machine Learning
data they and their partners hold while maintaining data subjects' privacy. In this thesis I show that private computation, such as private machine learning, can increase end-users' acceptance of data sharing practices, but not unconditionally. There are many factors that influence end-users' privacy perceptions in this space; including the number of organizations involved and the reciprocity of any data sharing practices. End-users emphasized the importance of detailing the purpose of a computation and clarifying that inputs to private computation are not shared across organizations. End-users also struggled with the notion of protections not being guaranteed 100\%, such as in statistical based schemes, thus demonstrating a need for a thorough understanding of the risk form attacks in such applications. When training a machine learning model on private data, it is critical to understand the conditions under which that data can be protected; and when it cannot. For instance, membership inference attacks aim to violate privacy protections by determining whether specific data was used to train a particular machine learning model.
Further, the successful transition of private machine learning theoretical research to practical use must account for gaps in achieving these properties that arise due to the realities of concrete implementations, threat models, and use cases; which is not currently the case
- …