<p>Knowledge Distillation (KD), which transfers the knowledge of a well-trained
large model (teacher) to a small model (student), has become an important area
of research for practical deployment of recommender systems. Recently, Relaxed
Ranking Distillation (RRD) has shown that distilling the ranking information in
the recommendation list significantly improves the performance. However, the
method still has limitations in that 1) it does not fully utilize the
prediction errors of the student model, which makes the training not fully
efficient, and 2) it only distills the user-side ranking information, which
provides an insufficient view under…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Industry recommender systems usually suffer from highly-skewed long-tail item
distributions where a small fraction of the items receives most of the user
feedback. This skew hurts recommender quality especially for the item slices
without much user feedback. While there have been many research advances made
in academia, deploying these methods in production is very difficult and very
few improvements have been made in industry. One challenge is that these
methods often hurt overall performance; additionally, they could be complex and
expensive to train and serve. In this work, we aim to improve tail item
recommendations while maintaining th…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>Automated Program Repair has attracted significant research in recent years,
leading to diverse techniques that focus on two main directions: search-based
and semantic-based program repair. The former techniques often face challenges
due to the vast search space, resulting in difficulties in identifying correct
solutions, while the latter approaches are constrained by the capabilities of
the underlying semantic analyser, limiting their scalability. In this paper, we
propose NEVERMORE, a novel learning-based mechanism inspired by the adversarial
nature of bugs and fixes. NEVERMORE is built upon the Generative Adversarial
Networks architectur…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>We consider multi-population Bayesian games with a large number of players.
Each player aims at minimizing a cost function that depends on this player's
own action, the distribution of players' actions in all populations, and an
unknown state parameter. We study the nonatomic limit versions of these games
and introduce the concept of Bayes correlated Wardrop equilibrium, which
extends the concept of Bayes correlated equilibrium to nonatomic games. We
prove that Bayes correlated Wardrop equilibria are limits of action flows
induced by Bayes correlated equilibria of the game with a large finite set of
small players. For nonatomic ga…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Human mobility prediction is a fundamental task essential for various
applications, including urban planning, location-based services and intelligent
transportation systems. Existing methods often ignore activity information
crucial for reasoning human preferences and routines, or adopt a simplified
representation of the dependencies between time, activities and locations. To
address these issues, we present Hierarchical Graph Attention Recurrent Network
(HGARN) for human mobility prediction. Specifically, we construct a
hierarchical graph based on all users' history mobility records and employ a
Hierarchical Graph Attention Module to …
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI
research. A better understanding of the needs of XAI users, as well as
human-centered evaluations of explainable models are both a necessity and a
challenge. In this paper, we explore how HCI and AI researchers conduct user
studies in XAI applications based on a systematic literature review. After
identifying and thoroughly analyzing 97core papers with human-based XAI
evaluations over the past five years, we categorize them along the measured
characteristics of explanatory methods, namely trust, understanding, usability,
and human-AI collaboration performance. Our…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>Machine Learning (ML) has been widely applied to cybersecurity and is
considered state-of-the-art for solving many of the open issues in that field.
However, it is very difficult to evaluate how good the produced solutions are,
since the challenges faced in security may not appear in other areas. One of
these challenges is the concept drift, which increases the existing arms race
between attackers and defenders: malicious actors can always create novel
threats to overcome the defense solutions, which may not consider them in some
approaches. Due to this, it is essential to know how to properly build and
evaluate an ML-based security solutio…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Phishing websites distribute unsolicited content and are frequently used to
commit email and internet fraud; detecting them before any user information is
submitted is critical. Several efforts have been made to detect these phishing
websites in recent years. Most existing approaches use hand-crafted lexical and
statistical features from a website's textual content to train classification
models to detect phishing web pages. However, these phishing detection
approaches have a few challenges, including 1) the tediousness of extracting
hand-crafted features, which require specialized domain knowledge to determine
which features are usefu…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>This paper develops an unified framework to study finite-sample convergence
guarantees of a large class of value-based asynchronous reinforcement learning
(RL) algorithms. We do this by first reformulating the RL algorithms as
\textit{Markovian Stochastic Approximation} (SA) algorithms to solve
fixed-point equations. We then develop a Lyapunov analysis and derive
mean-square error bounds on the convergence of the Markovian SA. Based on this
result, we establish finite-sample mean-square convergence bounds for
asynchronous RL algorithms such as $Q$-learning, $n$-step TD, TD$(\lambda)$,
and off-policy TD algorithms including V-trace. As a by-…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>We propose a new method in which a generative network (GN) integrate into a
reduced-order model (ROM) framework is used to solve inverse problems for
partial differential equations (PDE). The aim is to match available
measurements and estimate the corresponding uncertainties associated with the
states and parameters of a numerical physical simulation. The GN is trained
using only unconditional simulations of the discretized PDE model. We compare
the proposed method with the golden standard Markov chain Monte Carlo. We apply
the proposed approaches to a spatio-temporal compartmental model in
epidemiology. The results show that the proposed G…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Artificial intelligence (AI) has been widely applied in drug discovery with a
major task as molecular property prediction. Despite booming techniques in
molecular representation learning, key elements underlying molecular property
prediction remain largely unexplored, which impedes further advancements in
this field. Herein, we conduct an extensive evaluation of representative models
using various representations on the MoleculeNet datasets, a suite of
opioids-related datasets and two additional activity datasets from the
literature. To investigate the predictive power in low-data and high-data
space, a series of descriptors datasets of var…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>Many partial differential equations in mathematical physics describe the
evolution of a time-dependent vector field. Examples arise in compressible
fluid dynamics, shape analysis, optimal transport and shallow water equations.
The flow of such a vector field generates a diffeomorphism, which can be viewed
as the Lagrangian variable corresponding to the Eulerian vector field. From
both computational and theoretical perspectives, it is natural to seek
finite-dimensional analogs of vector fields and diffeomorphisms, constructed in
such a way that the underlying geometric and algebraic properties persist (in
particular, the induced Lie--Poisson…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>Data-driven evolutionary algorithms usually aim to exploit the information
behind a limited amount of data to perform optimization, which have proved to
be successful in solving many complex real-world optimization problems.
However, most data-driven evolutionary algorithms are centralized, causing
privacy and security concerns. Existing federated Bayesian algorithms and
data-driven evolutionary algorithms mainly protect the raw data on each client.
To address this issue, this paper proposes a secure federated data-driven
evolutionary multi-objective optimization algorithm to protect both the raw
data and the newly infilled solutions obtain…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>We consider the problem of computing the Maximal Exact Matches (MEMs) of a
given pattern $P[1 .. m]$ on a large repetitive text collection $T[1 .. n]$,
which is represented as a (hopefully much smaller) run-length context-free
grammar of size $g_{rl}$. We show that the problem can be solved in time $O(m^2
\log^\epsilon n)$, for any constant $\epsilon > 0$, on a data structure of size
$O(g_{rl})$. Further, on a locally consistent grammar of size
$O(\delta\log\frac{n}{\delta})$, the time decreases to $O(m\log m(\log m +
\log^\epsilon n))$. The value $\delta$ is a function of the substring
complexity of $T$ and $\Omega(\delta\log\frac{n…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>We study the lifetime of locally stable states in the Thirring model, which
describes a system of particles whose interactions are long-range. The model
exhibits first-order phase transitions in the canonical ensemble and,
therefore, a free energy barrier separates two free energy minima. The energy
of the system diffuses as a result of thermal fluctuations and we show that its
dynamics can be described by means of a Fokker-Planck equation. Considering an
initial state where the energy takes the value corresponding to one of the
minima of the free energy, we can define the lifetime of the initial state as
the mean first-passage time for the…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>We argue for the application of bibliometric indices to quantify the
long-term uncertainty of outcome in sports. The Euclidean index is proposed to
reward quality over quantity, while the rectangle index can be an appropriate
measure of core performance. Their differences are highlighted through an
axiomatic analysis and several examples. Our approach also requires a weighting
scheme to compare different achievements. The methodology is illustrated by
studying the knockout stage of the UEFA Champions League in the 20 seasons
played between 2003 and 2023: club and country performances as well as three
types of competitive balance are conside…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>The issue of potential privacy leakage during centralized AI's model training
has drawn intensive concern from the public. A Parallel and Distributed
Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new
paradigm to cope with the privacy issue by allowing clients to perform model
training locally, without the necessity to upload their personal sensitive
data. In FL, the number of clients could be sufficiently large, but the
bandwidth available for model distribution and re-upload is quite limited,
making it sensible to only involve part of the volunteers to participate in the
training process. The client selec…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>We develop investment approaches to secure electric power systems against
load attacks where a malicious intruder (the attacker) covertly changes
reactive power setpoints of loads to push the grid towards voltage instability
while the system operator (the defender) employs reactive power compensation
(RPC) to prevent instability. Extending our previously reported Stackelberg
game formulation for this problem, we develop a robust-defense sequential
algorithm and a novel genetic algorithm that provides scalability to
large-scale power system models. The proposed methods are validated using IEEE
prototype power system models with time-varying …
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>The development of Graph Representation Learning methods for heterogeneous
graphs is fundamental in several real-world applications, since in several
contexts graphs are characterized by different types of nodes and edges. We
introduce a an algorithmic framework (Het-node2vec) that extends the original
node2vec node-neighborhood sampling method to heterogeneous multigraphs. The
resulting random walk samples capture both the structural characteristics of
the graph and the semantics of the different types of nodes and edges. The
proposed algorithms can focus their attention on specific node or edge types,
allowing accurate representations als…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Increased penetration of Distributed Energy Resources (DER) and Renewable
Energy Systems (RES) transforming the conventional distribution grid into a
transactive framework supervised by a distribution system operator (DSO).
Although the emerging transactive energy management techniques improve the grid
reliability, the inherent uncertainty of RES poses a challenge in meeting the
power demand of the critical infrastructure in the microgrid unless sufficient
battery energy storage is maintained. However, maintaining expensive battery
storage increases the operating cost of the DSO. In this article, we propose a
cost-effective dynamic resource…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Conditional Normalizing Flows (CNFs) are flexible generative models capable
of representing complicated distributions with high dimensionality and large
interdimensional correlations, making them appealing for structured output
learning. Their effectiveness in modelling multivariates spatio-temporal
structured data has yet to be completely investigated. We propose MotionFlow as
a novel normalizing flows approach that autoregressively conditions the output
distributions on the spatio-temporal input features. It combines deterministic
and stochastic representations with CNFs to create a probabilistic neural
generative approach that can model …
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>Network pruning is an effective approach to reduce network complexity with
acceptable performance compromise. Existing studies achieve the sparsity of
neural networks via time-consuming weight training or complex searching on
networks with expanded width, which greatly limits the applications of network
pruning. In this paper, we show that high-performing and sparse sub-networks
without the involvement of weight training, termed "lottery jackpots", exist in
pre-trained models with unexpanded width. Furthermore, we improve the
efficiency for searching lottery jackpots from two perspectives. Firstly, we
observe that the sparse masks…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:31 a.m.
<p>This paper proposes a combined optimization and learning method for
impact-friendly, non-prehensile catching of objects at non-zero velocity.
Through a constrained Quadratic Programming problem, the method generates
optimal trajectories up to the contact point between the robot and the object
to minimize their relative velocity and reduce the impact forces. Next, the
generated trajectories are updated by Kernelized Movement Primitives, which are
based on human catching demonstrations to ensure a smooth transition around the
catching point. In addition, the learned human variable stiffness (HVS) is sent
to the robot's Cartesian impedanc…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>Audio is one of the most used ways of human communication, but at the same
time it can be easily misused to trick people. With the revolution of AI, the
related technologies are now accessible to almost everyone thus making it
simple for the criminals to commit crimes and forgeries. In this work, we
introduce a neural network method to develop a classifier that will blindly
classify an input audio as real or mimicked; the word 'blindly' refers to the
ability to detect mimicked audio without references or real sources. The
proposed model was trained on a set of important features extracted from a
large dataset of audios to get a cl…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
<p>We present a scalable strategy for development of mesh-free hybrid
neuro-symbolic partial differential equation solvers based on existing
mesh-based numerical discretization methods. Particularly, this strategy can be
used to efficiently train neural network surrogate models of partial
differential equations by (i) leveraging the accuracy and convergence
properties of advanced numerical methods, solvers, and preconditioners, as well
as (ii) better scalability to higher order PDEs by strictly limiting
optimization to first order automatic differentiation. The presented neural
bootstrapping method (hereby dubbed NBM) is based on evaluation of…
Voters: 0
Views: 0
Latest: Sept. 6, 2023, 7:32 a.m.
