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2307.03115 | Zijun Yao | Zijun Yao, Yantao Liu, Xin Lv, Shulin Cao, Jifan Yu, Lei Hou, Juanzi
Li | KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text
Understanding | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep text understanding, which requires the connections between a given
document and prior knowledge beyond its text, has been highlighted by many
benchmarks in recent years. However, these benchmarks have encountered two
major limitations. On the one hand, most of them require human annotation of
knowledge, which leads to limited knowledge coverage. On the other hand, they
usually use choices or spans in the texts as the answers, which results in
narrow answer space. To overcome these limitations, we build a new challenging
benchmark named KoRc in this paper. Compared with previous benchmarks, KoRC has
two advantages, i.e., broad knowledge coverage and flexible answer format.
Specifically, we utilize massive knowledge bases to guide annotators or large
language models (LLMs) to construct knowledgable questions. Moreover, we use
labels in knowledge bases rather than spans or choices as the final answers. We
test state-of-the-art models on KoRC and the experimental results show that the
strongest baseline only achieves 68.3% and 30.0% F1 measure in the
in-distribution and out-of-distribution test set, respectively. These results
indicate that deep text understanding is still an unsolved challenge. The
benchmark dataset, leaderboard, and baseline methods are released in
https://github.com/THU-KEG/KoRC.
| [
{
"created": "Thu, 6 Jul 2023 16:35:25 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yao",
"Zijun",
""
],
[
"Liu",
"Yantao",
""
],
[
"Lv",
"Xin",
""
],
[
"Cao",
"Shulin",
""
],
[
"Yu",
"Jifan",
""
],
[
"Hou",
"Lei",
""
],
[
"Li",
"Juanzi",
""
]
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2307.03118 | Sagnik Chatterjee | Sagnik Chatterjee and Vyacheslav Kungurtsev | Quantum Solutions to the Privacy vs. Utility Tradeoff | null | null | null | null | quant-ph cs.CR cs.LG | http://creativecommons.org/licenses/by/4.0/ | In this work, we propose a novel architecture (and several variants thereof)
based on quantum cryptographic primitives with provable privacy and security
guarantees regarding membership inference attacks on generative models. Our
architecture can be used on top of any existing classical or quantum generative
models. We argue that the use of quantum gates associated with unitary
operators provides inherent advantages compared to standard Differential
Privacy based techniques for establishing guaranteed security from all
polynomial-time adversaries.
| [
{
"created": "Thu, 6 Jul 2023 16:43:58 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Chatterjee",
"Sagnik",
""
],
[
"Kungurtsev",
"Vyacheslav",
""
]
] | [
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] |
2307.03119 | Kan Ren | Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang
Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu | Learning Multi-Agent Intention-Aware Communication for Optimal
Multi-Order Execution in Finance | Accepted in KDD 2023; The website is at
https://seqml.github.io/marl4fin | null | 10.1145/3580305.3599856 | null | cs.AI cs.LG cs.MA | http://creativecommons.org/licenses/by/4.0/ | Order execution is a fundamental task in quantitative finance, aiming at
finishing acquisition or liquidation for a number of trading orders of the
specific assets. Recent advance in model-free reinforcement learning (RL)
provides a data-driven solution to the order execution problem. However, the
existing works always optimize execution for an individual order, overlooking
the practice that multiple orders are specified to execute simultaneously,
resulting in suboptimality and bias. In this paper, we first present a
multi-agent RL (MARL) method for multi-order execution considering practical
constraints. Specifically, we treat every agent as an individual operator to
trade one specific order, while keeping communicating with each other and
collaborating for maximizing the overall profits. Nevertheless, the existing
MARL algorithms often incorporate communication among agents by exchanging only
the information of their partial observations, which is inefficient in
complicated financial market. To improve collaboration, we then propose a
learnable multi-round communication protocol, for the agents communicating the
intended actions with each other and refining accordingly. It is optimized
through a novel action value attribution method which is provably consistent
with the original learning objective yet more efficient. The experiments on the
data from two real-world markets have illustrated superior performance with
significantly better collaboration effectiveness achieved by our method.
| [
{
"created": "Thu, 6 Jul 2023 16:45:40 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Fang",
"Yuchen",
""
],
[
"Tang",
"Zhenggang",
""
],
[
"Ren",
"Kan",
""
],
[
"Liu",
"Weiqing",
""
],
[
"Zhao",
"Li",
""
],
[
"Bian",
"Jiang",
""
],
[
"Li",
"Dongsheng",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Yu",
"Yong",
""
],
[
"Liu",
"Tie-Yan",
""
]
] | [
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] |
2307.03122 | Sneha Singhania | Sneha Singhania, Simon Razniewski, Gerhard Weikum | Extracting Multi-valued Relations from Language Models | Accepted to Repl4NLP Workshop at ACL 2023 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | The widespread usage of latent language representations via pre-trained
language models (LMs) suggests that they are a promising source of structured
knowledge. However, existing methods focus only on a single object per
subject-relation pair, even though often multiple objects are correct. To
overcome this limitation, we analyze these representations for their potential
to yield materialized multi-object relational knowledge. We formulate the
problem as a rank-then-select task. For ranking candidate objects, we evaluate
existing prompting techniques and propose new ones incorporating domain
knowledge. Among the selection methods, we find that choosing objects with a
likelihood above a learned relation-specific threshold gives a 49.5% F1 score.
Our results highlight the difficulty of employing LMs for the multi-valued
slot-filling task and pave the way for further research on extracting
relational knowledge from latent language representations.
| [
{
"created": "Thu, 6 Jul 2023 16:48:32 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Singhania",
"Sneha",
""
],
[
"Razniewski",
"Simon",
""
],
[
"Weikum",
"Gerhard",
""
]
] | [
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] | Extracting Multi-valued Relations from Language Models | https://arxiv.org/abs/2307.03122v2 | https://arxiv.org/pdf/2307.03122v2.pdf | https://github.com/snehasinghania/multi_valued_slot_filling | true | true | https://paperswithcode.com/paper/extracting-multi-valued-relations-from | [
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2307.03123 | Yannick Couzini\'e | Yannick Couzinie, Yusuke Nishiya, Hirofumi Nishi, Taichi Kosugi,
Yu-ichiro Matsushita | Annealing for prediction of grand canonical crystal structures:
Efficient implementation of n-body atomic interactions | 7 pages, 4 figures, 1 tables | null | null | null | quant-ph cond-mat.dis-nn physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | We propose an annealing scheme for crystal structures prediction (CSP) by
taking into account the general n-body atomic interactions, and in particular
three-body interactions which are necessary to simulate covalent bonds. The
crystal structure is represented by discretizing the real space by mesh and
placing binary variables which express the existence or non-existence of an
atom on every grid point. We implement n-body atomic interaction in quadratic
unconstrained binary optimization (QUBO) or higher-order unconstrained binary
optimization (HUBO) problems and perform CSP by simulated annealing. In this
study we successfully reduce the number of bits necessary to implement
three-body interactions within the HUBO formulation of MoS2 crystals. Further,
we find that grand canonical simulation is possible by showing that we can
simultaneously optimize for the particle density as well as the crystal
structure using simulated annealing. In particular, we apply CSP to noble
gasses, i.e. Lennard-Jones(LJ) solids, and show that the grand canonical
calculation has a better time to solution scaling than its microcanonical
counterpart.
| [
{
"created": "Thu, 6 Jul 2023 16:49:06 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Couzinie",
"Yannick",
""
],
[
"Nishiya",
"Yusuke",
""
],
[
"Nishi",
"Hirofumi",
""
],
[
"Kosugi",
"Taichi",
""
],
[
"Matsushita",
"Yu-ichiro",
""
]
] | [
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2307.03126 | Mattia Giovanni Campana | Valerio Arnaboldi, Mattia Giovanni Campana, Franca Delmastro | Context-Aware Configuration and Management of WiFi Direct Groups for
Real Opportunistic Networks | Accepted by the IEEE 14th International Conference on Mobile Ad Hoc
and Sensor Systems (MASS), 2017 | null | 10.1109/MASS.2017.40 | null | cs.NI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wi-Fi Direct is a promising technology for the support of device-to-device
communications (D2D) on commercial mobile devices. However, the standard
as-it-is is not sufficient to support the real deployment of networking
solutions entirely based on D2D such as opportunistic networks. In fact, WiFi
Direct presents some characteristics that could limit the autonomous creation
of D2D connections among users' personal devices. Specifically, the standard
explicitly requires the user's authorization to establish a connection between
two or more devices, and it provides a limited support for inter-group
communication. In some cases, this might lead to the creation of isolated
groups of nodes which cannot communicate among each other. In this paper, we
propose a novel middleware-layer protocol for the efficient configuration and
management of WiFi Direct groups (WiFi Direct Group Manager, WFD-GM) to enable
autonomous connections and inter-group communication. This enables
opportunistic networks in real conditions (e.g., variable mobility and network
size). WFD-GM defines a context function that takes into account heterogeneous
parameters for the creation of the best group configuration in a specific time
window, including an index of nodes' stability and power levels. We evaluate
the protocol performances by simulating three reference scenarios including
different mobility models, geographical areas and number of nodes. Simulations
are also supported by experimental results related to the evaluation in a real
testbed of the involved context parameters. We compare WFD-GM with the
state-of-the-art solutions and we show that it performs significantly better
than a Baseline approach in scenarios with medium/low mobility, and it is
comparable with it in case of high mobility, without introducing additional
overhead.
| [
{
"created": "Thu, 6 Jul 2023 16:52:20 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Arnaboldi",
"Valerio",
""
],
[
"Campana",
"Mattia Giovanni",
""
],
[
"Delmastro",
"Franca",
""
]
] | [
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] |
2307.03128 | Morten Akh{\o}j Pedersen | Morten Akh{\o}j, James Benn, Erlend Grong, Stefan Sommer, Xavier
Pennec | Principal subbundles for dimension reduction | null | null | null | null | stat.ME cs.CV cs.LG math.DG math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we demonstrate how sub-Riemannian geometry can be used for
manifold learning and surface reconstruction by combining local linear
approximations of a point cloud to obtain lower dimensional bundles. Local
approximations obtained by local PCAs are collected into a rank $k$ tangent
subbundle on $\mathbb{R}^d$, $k<d$, which we call a principal subbundle. This
determines a sub-Riemannian metric on $\mathbb{R}^d$. We show that
sub-Riemannian geodesics with respect to this metric can successfully be
applied to a number of important problems, such as: explicit construction of an
approximating submanifold $M$, construction of a representation of the
point-cloud in $\mathbb{R}^k$, and computation of distances between
observations, taking the learned geometry into account. The reconstruction is
guaranteed to equal the true submanifold in the limit case where tangent spaces
are estimated exactly. Via simulations, we show that the framework is robust
when applied to noisy data. Furthermore, the framework generalizes to
observations on an a priori known Riemannian manifold.
| [
{
"created": "Thu, 6 Jul 2023 16:55:21 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Akhøj",
"Morten",
""
],
[
"Benn",
"James",
""
],
[
"Grong",
"Erlend",
""
],
[
"Sommer",
"Stefan",
""
],
[
"Pennec",
"Xavier",
""
]
] | [
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2307.03130 | Zijun Yao | Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu,
Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li | VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge
Base Question Answering | null | null | null | null | cs.CL cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present Visual Knowledge oriented Programming platform (VisKoP), a
knowledge base question answering (KBQA) system that integrates human into the
loop to edit and debug the knowledge base (KB) queries. VisKoP not only
provides a neural program induction module, which converts natural language
questions into knowledge oriented program language (KoPL), but also maps KoPL
programs into graphical elements. KoPL programs can be edited with simple
graphical operators, such as dragging to add knowledge operators and slot
filling to designate operator arguments. Moreover, VisKoP provides
auto-completion for its knowledge base schema and users can easily debug the
KoPL program by checking its intermediate results. To facilitate the practical
KBQA on a million-entity-level KB, we design a highly efficient KoPL execution
engine for the back-end. Experiment results show that VisKoP is highly
efficient and user interaction can fix a large portion of wrong KoPL programs
to acquire the correct answer. The VisKoP online demo
https://demoviskop.xlore.cn (Stable release of this paper) and
https://viskop.xlore.cn (Beta release with new features), highly efficient KoPL
engine https://pypi.org/project/kopl-engine, and screencast video
https://youtu.be/zAbJtxFPTXo are now publicly available.
| [
{
"created": "Thu, 6 Jul 2023 16:58:27 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yao",
"Zijun",
""
],
[
"Chen",
"Yuanyong",
""
],
[
"Lv",
"Xin",
""
],
[
"Cao",
"Shulin",
""
],
[
"Xin",
"Amy",
""
],
[
"Yu",
"Jifan",
""
],
[
"Jin",
"Hailong",
""
],
[
"Xu",
"Jianjun",
""
],
[
"Zhang",
"Peng",
""
],
[
"Hou",
"Lei",
""
],
[
"Li",
"Juanzi",
""
]
] | [
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] |
2307.03131 | Yiming Yan | Yiming Yan, Tao Wang, Chengqi Zhao, Shujian Huang, Jiajun Chen,
Mingxuan Wang | BLEURT Has Universal Translations: An Analysis of Automatic Metrics by
Minimum Risk Training | Accepted to ACL 2023 main conference | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic metrics play a crucial role in machine translation. Despite the
widespread use of n-gram-based metrics, there has been a recent surge in the
development of pre-trained model-based metrics that focus on measuring sentence
semantics. However, these neural metrics, while achieving higher correlations
with human evaluations, are often considered to be black boxes with potential
biases that are difficult to detect. In this study, we systematically analyze
and compare various mainstream and cutting-edge automatic metrics from the
perspective of their guidance for training machine translation systems. Through
Minimum Risk Training (MRT), we find that certain metrics exhibit robustness
defects, such as the presence of universal adversarial translations in BLEURT
and BARTScore. In-depth analysis suggests two main causes of these robustness
deficits: distribution biases in the training datasets, and the tendency of the
metric paradigm. By incorporating token-level constraints, we enhance the
robustness of evaluation metrics, which in turn leads to an improvement in the
performance of machine translation systems. Codes are available at
\url{https://github.com/powerpuffpomelo/fairseq_mrt}.
| [
{
"created": "Thu, 6 Jul 2023 16:59:30 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yan",
"Yiming",
""
],
[
"Wang",
"Tao",
""
],
[
"Zhao",
"Chengqi",
""
],
[
"Huang",
"Shujian",
""
],
[
"Chen",
"Jiajun",
""
],
[
"Wang",
"Mingxuan",
""
]
] | [
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] | BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training | https://arxiv.org/abs/2307.03131v2 | https://arxiv.org/pdf/2307.03131v2.pdf | https://github.com/powerpuffpomelo/fairseq_mrt | true | true | https://paperswithcode.com/paper/bleurt-has-universal-translations-an-analysis | [
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2307.03132 | Pratyush Maini | Pratyush Maini, Sachin Goyal, Zachary C. Lipton, J. Zico Kolter, Aditi
Raghunathan | T-MARS: Improving Visual Representations by Circumventing Text Feature
Learning | null | null | null | null | cs.CV cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large web-sourced multimodal datasets have powered a slew of new methods for
learning general-purpose visual representations, advancing the state of the art
in computer vision and revolutionizing zero- and few-shot recognition. One
crucial decision facing practitioners is how, if at all, to curate these
ever-larger datasets. For example, the creators of the LAION-5B dataset chose
to retain only image-caption pairs whose CLIP similarity score exceeded a
designated threshold. In this paper, we propose a new state-of-the-art data
filtering approach motivated by our observation that nearly 40% of LAION's
images contain text that overlaps significantly with the caption. Intuitively,
such data could be wasteful as it incentivizes models to perform optical
character recognition rather than learning visual features. However, naively
removing all such data could also be wasteful, as it throws away images that
contain visual features (in addition to overlapping text). Our simple and
scalable approach, T-MARS (Text Masking and Re-Scoring), filters out only those
pairs where the text dominates the remaining visual features -- by first
masking out the text and then filtering out those with a low CLIP similarity
score of the masked image. Experimentally, T-MARS outperforms the top-ranked
method on the "medium scale" of DataComp (a data filtering benchmark) by a
margin of 6.5% on ImageNet and 4.7% on VTAB. Additionally, our systematic
evaluation on various data pool sizes from 2M to 64M shows that the accuracy
gains enjoyed by T-MARS linearly increase as data and compute are scaled
exponentially. Code is available at https://github.com/locuslab/T-MARS.
| [
{
"created": "Thu, 6 Jul 2023 16:59:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Maini",
"Pratyush",
""
],
[
"Goyal",
"Sachin",
""
],
[
"Lipton",
"Zachary C.",
""
],
[
"Kolter",
"J. Zico",
""
],
[
"Raghunathan",
"Aditi",
""
]
] | [
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] | T-MARS: Improving Visual Representations by Circumventing Text Feature Learning | https://arxiv.org/abs/2307.03132v1 | https://arxiv.org/pdf/2307.03132v1.pdf | https://github.com/locuslab/t-mars | true | true | https://paperswithcode.com/paper/t-mars-improving-visual-representations-by | [
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] |
2307.03133 | Yongcan Yu | Yongcan Yu, Lijun Sheng, Ran He, Jian Liang | Benchmarking Test-Time Adaptation against Distribution Shifts in Image
Classification | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Test-time adaptation (TTA) is a technique aimed at enhancing the
generalization performance of models by leveraging unlabeled samples solely
during prediction. Given the need for robustness in neural network systems when
faced with distribution shifts, numerous TTA methods have recently been
proposed. However, evaluating these methods is often done under different
settings, such as varying distribution shifts, backbones, and designing
scenarios, leading to a lack of consistent and fair benchmarks to validate
their effectiveness. To address this issue, we present a benchmark that
systematically evaluates 13 prominent TTA methods and their variants on five
widely used image classification datasets: CIFAR-10-C, CIFAR-100-C, ImageNet-C,
DomainNet, and Office-Home. These methods encompass a wide range of adaptation
scenarios (e.g. online adaptation v.s. offline adaptation, instance adaptation
v.s. batch adaptation v.s. domain adaptation). Furthermore, we explore the
compatibility of different TTA methods with diverse network backbones. To
implement this benchmark, we have developed a unified framework in PyTorch,
which allows for consistent evaluation and comparison of the TTA methods across
the different datasets and network architectures. By establishing this
benchmark, we aim to provide researchers and practitioners with a reliable
means of assessing and comparing the effectiveness of TTA methods in improving
model robustness and generalization performance. Our code is available at
https://github.com/yuyongcan/Benchmark-TTA.
| [
{
"created": "Thu, 6 Jul 2023 16:59:53 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yu",
"Yongcan",
""
],
[
"Sheng",
"Lijun",
""
],
[
"He",
"Ran",
""
],
[
"Liang",
"Jian",
""
]
] | [
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] | Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification | https://arxiv.org/abs/2307.03133v1 | https://arxiv.org/pdf/2307.03133v1.pdf | https://github.com/yuyongcan/benchmark-tta | true | true | https://paperswithcode.com/paper/benchmarking-test-time-adaptation-against | [
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2307.03135 | Xuanlin Li | Xuanlin Li, Yunhao Fang, Minghua Liu, Zhan Ling, Zhuowen Tu, Hao Su | Distilling Large Vision-Language Model with Out-of-Distribution
Generalizability | null | null | null | null | cs.CV cs.AI cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Large vision-language models have achieved outstanding performance, but their
size and computational requirements make their deployment on
resource-constrained devices and time-sensitive tasks impractical. Model
distillation, the process of creating smaller, faster models that maintain the
performance of larger models, is a promising direction towards the solution.
This paper investigates the distillation of visual representations in large
teacher vision-language models into lightweight student models using a small-
or mid-scale dataset. Notably, this study focuses on open-vocabulary
out-of-distribution (OOD) generalization, a challenging problem that has been
overlooked in previous model distillation literature. We propose two principles
from vision and language modality perspectives to enhance student's OOD
generalization: (1) by better imitating teacher's visual representation space,
and carefully promoting better coherence in vision-language alignment with the
teacher; (2) by enriching the teacher's language representations with
informative and finegrained semantic attributes to effectively distinguish
between different labels. We propose several metrics and conduct extensive
experiments to investigate their techniques. The results demonstrate
significant improvements in zero-shot and few-shot student performance on
open-vocabulary out-of-distribution classification, highlighting the
effectiveness of our proposed approaches. Our code will be released at
https://github.com/xuanlinli17/large_vlm_distillation_ood
| [
{
"created": "Thu, 6 Jul 2023 17:05:26 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Xuanlin",
""
],
[
"Fang",
"Yunhao",
""
],
[
"Liu",
"Minghua",
""
],
[
"Ling",
"Zhan",
""
],
[
"Tu",
"Zhuowen",
""
],
[
"Su",
"Hao",
""
]
] | [
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] |
2307.03136 | Wayne Lin | Ilayda Canyakmaz, Wayne Lin, Georgios Piliouras, Antonios Varvitsiotis | Multiplicative Updates for Online Convex Optimization over Symmetric
Cones | 27 pages, 7 figures, 2 tables | null | null | null | math.OC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study online convex optimization where the possible actions are trace-one
elements in a symmetric cone, generalizing the extensively-studied experts
setup and its quantum counterpart. Symmetric cones provide a unifying framework
for some of the most important optimization models, including linear,
second-order cone, and semidefinite optimization. Using tools from the field of
Euclidean Jordan Algebras, we introduce the Symmetric-Cone Multiplicative
Weights Update (SCMWU), a projection-free algorithm for online optimization
over the trace-one slice of an arbitrary symmetric cone. We show that SCMWU is
equivalent to Follow-the-Regularized-Leader and Online Mirror Descent with
symmetric-cone negative entropy as regularizer. Using this structural result we
show that SCMWU is a no-regret algorithm, and verify our theoretical results
with extensive experiments. Our results unify and generalize the analysis for
the Multiplicative Weights Update method over the probability simplex and the
Matrix Multiplicative Weights Update method over the set of density matrices.
| [
{
"created": "Thu, 6 Jul 2023 17:06:43 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Canyakmaz",
"Ilayda",
""
],
[
"Lin",
"Wayne",
""
],
[
"Piliouras",
"Georgios",
""
],
[
"Varvitsiotis",
"Antonios",
""
]
] | [
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] |
2307.03137 | Seher Ozcelik | Seher Ozcelik, Sinan Unver, Ilke Ali Gurses, Rustu Turkay, and Cigdem
Gunduz-Demir | Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed
Tomography Images | null | null | null | null | eess.IV cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Segmentation networks are not explicitly imposed to learn global invariants
of an image, such as the shape of an object and the geometry between multiple
objects, when they are trained with a standard loss function. On the other
hand, incorporating such invariants into network training may help improve
performance for various segmentation tasks when they are the intrinsic
characteristics of the objects to be segmented. One example is segmentation of
aorta and great vessels in computed tomography (CT) images where vessels are
found in a particular geometry in the body due to the human anatomy and they
mostly seem as round objects on a 2D CT image. This paper addresses this issue
by introducing a new topology-aware loss function that penalizes topology
dissimilarities between the ground truth and prediction through persistent
homology. Different from the previously suggested segmentation network designs,
which apply the threshold filtration on a likelihood function of the prediction
map and the Betti numbers of the ground truth, this paper proposes to apply the
Vietoris-Rips filtration to obtain persistence diagrams of both ground truth
and prediction maps and calculate the dissimilarity with the Wasserstein
distance between the corresponding persistence diagrams. The use of this
filtration has advantage of modeling shape and geometry at the same time, which
may not happen when the threshold filtration is applied. Our experiments on
4327 CT images of 24 subjects reveal that the proposed topology-aware loss
function leads to better results than its counterparts, indicating the
effectiveness of this use.
| [
{
"created": "Thu, 6 Jul 2023 17:06:49 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ozcelik",
"Seher",
""
],
[
"Unver",
"Sinan",
""
],
[
"Gurses",
"Ilke Ali",
""
],
[
"Turkay",
"Rustu",
""
],
[
"Gunduz-Demir",
"Cigdem",
""
]
] | [
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2307.03148 | Andrea Araldo | Severin Diepolder, Andrea Araldo, Tarek Chouaki, Santa Maiti,
Sebastian H\"orl, Costantinos Antoniou | On the Computation of Accessibility Provided by Shared Mobility | null | hEART 2023: 11th Symposium of the European Association for
Research in Transportation | null | null | cs.CY cs.NA math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shared Mobility Services (SMS), e.g., Demand-Responsive Transit (DRT) or
ride-sharing, can improve mobility in low-density areas, often poorly served by
conventional Public Transport (PT). Such improvement is mostly quantified via
basic performance indicators, like wait or travel time. However, accessibility
indicators, measuring the ease of reaching surrounding opportunities (e.g.,
jobs, schools, shops, ...), would be a more comprehensive indicator. To date,
no method exists to quantify the accessibility of SMS based on empirical
measurements. Indeed, accessibility is generally computed on graph
representations of PT networks, but SMS are dynamic and do not follow a
predefined network. We propose a spatial-temporal statistical method that takes
as input observed trips of a SMS acting as a feeder for PT and summarized such
trips in a graph. On such a graph, we compute classic accessibility indicators.
We apply our method to a MATSim simulation study concerning DRT in
Paris-Saclay.
| [
{
"created": "Thu, 6 Jul 2023 17:23:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Diepolder",
"Severin",
""
],
[
"Araldo",
"Andrea",
""
],
[
"Chouaki",
"Tarek",
""
],
[
"Maiti",
"Santa",
""
],
[
"Hörl",
"Sebastian",
""
],
[
"Antoniou",
"Costantinos",
""
]
] | [
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2307.03153 | Kate Sanders | Kate Sanders, David Etter, Reno Kriz, Benjamin Van Durme | MultiVENT: Multilingual Videos of Events with Aligned Natural Text | null | null | null | null | cs.IR cs.CV cs.MM | http://creativecommons.org/licenses/by/4.0/ | Everyday news coverage has shifted from traditional broadcasts towards a wide
range of presentation formats such as first-hand, unedited video footage.
Datasets that reflect the diverse array of multimodal, multilingual news
sources available online could be used to teach models to benefit from this
shift, but existing news video datasets focus on traditional news broadcasts
produced for English-speaking audiences. We address this limitation by
constructing MultiVENT, a dataset of multilingual, event-centric videos
grounded in text documents across five target languages. MultiVENT includes
both news broadcast videos and non-professional event footage, which we use to
analyze the state of online news videos and how they can be leveraged to build
robust, factually accurate models. Finally, we provide a model for complex,
multilingual video retrieval to serve as a baseline for information retrieval
using MultiVENT.
| [
{
"created": "Thu, 6 Jul 2023 17:29:34 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Sanders",
"Kate",
""
],
[
"Etter",
"David",
""
],
[
"Kriz",
"Reno",
""
],
[
"Van Durme",
"Benjamin",
""
]
] | [
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] |
2307.03157 | Janet Wang | Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm | Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion
Classification? | null | null | null | null | cs.CV cs.CY cs.LG | http://creativecommons.org/licenses/by/4.0/ | Deep learning-based diagnostic system has demonstrated potential in
classifying skin cancer conditions when labeled training example are abundant.
However, skin lesion analysis often suffers from a scarcity of labeled data,
hindering the development of an accurate and reliable diagnostic system. In
this work, we leverage multiple skin lesion datasets and investigate the
feasibility of various unsupervised domain adaptation (UDA) methods in binary
and multi-class skin lesion classification. In particular, we assess three UDA
training schemes: single-, combined-, and multi-source. Our experiment results
show that UDA is effective in binary classification, with further improvement
being observed when imbalance is mitigated. In multi-class task, its
performance is less prominent, and imbalance problem again needs to be
addressed to achieve above-baseline accuracy. Through our quantitative
analysis, we find that the test error of multi-class tasks is strongly
correlated with label shift, and feature-level UDA methods have limitations
when handling imbalanced datasets. Finally, our study reveals that UDA can
effectively reduce bias against minority groups and promote fairness, even
without the explicit use of fairness-focused techniques.
| [
{
"created": "Thu, 6 Jul 2023 17:32:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Janet",
""
],
[
"Zhang",
"Yunbei",
""
],
[
"Ding",
"Zhengming",
""
],
[
"Hamm",
"Jihun",
""
]
] | [
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2307.03159 | Bartosz Protas | Bartosz Protas | On the Linear Stability of the Lamb-Chaplygin Dipole | 33 pages, 10 figures | null | null | null | physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | The Lamb-Chaplygin dipole (Lamb1895,Lamb1906,Chaplygin1903) is one of the few
closed-form relative equilibrium solutions of the 2D Euler equation
characterized by a continuous vorticity distribution. We consider the problem
of its linear stability with respect to 2D circulation-preserving
perturbations. It is demonstrated that this flow is linearly unstable, although
the nature of this instability is subtle and cannot be fully understood without
accounting for infinite-dimensional aspects of the problem. To elucidate this,
we first derive a convenient form of the linearized Euler equation defined
within the vortex core which accounts for the potential flow outside the core
while making it possible to track deformations of the vortical region. The
linear stability of the flow is then determined by the spectrum of the
corresponding operator. Asymptotic analysis of the associated eigenvalue
problem shows the existence of approximate eigenfunctions in the form of
short-wavelength oscillations localized near the boundary of the vortex and
these findings are confirmed by the numerical solution of the eigenvalue
problem. However, the time-integration of the 2D Euler system reveals the
existence of only one linearly unstable eigenmode and since the corresponding
eigenvalue is embedded in the essential spectrum of the operator, this unstable
eigenmode is also shown to be a distribution characterized by short-wavelength
oscillations rather than a smooth function. These findings are consistent with
the general results known about the stability of equilibria in 2D Euler flows
and have been verified by performing computations with different numerical
resolutions and arithmetic precisions.
| [
{
"created": "Thu, 6 Jul 2023 17:38:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Protas",
"Bartosz",
""
]
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] |
2307.03162 | Yao Shi | Yao Shi, Xiaofeng Zhang, Ran zhang, Zhou Yang, Xiao Tang, Hongni Ye,
Yi Wu | BrickPal: Augmented Reality-based Assembly Instructions for Brick Models | 9 pages,7 figures. Project URL: https://origami.dance/brickpal | null | null | null | cs.HC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The assembly instruction is a mandatory component of Lego-like brick sets.The
conventional production of assembly instructions requires a considerable amount
of manual fine-tuning, which is intractable for casual users and customized
brick sets.Moreover, the traditional paper-based instructions lack
expressiveness and interactivity.To tackle the two problems above, we present
BrickPal, an augmented reality-based system, which visualizes assembly
instructions in an augmented reality head-mounted display. It utilizes Natural
Language Processing (NLP) techniques to generate plausible assembly sequences,
and provide real-time guidance in the AR headset.Our user study demonstrates
BrickPal's effectiveness at assisting users in brick assembly compared to
traditional assembly methods. Additionally, the NLP algorithm-generated
assembly sequences achieve the same usability with manually adapted sequences.
| [
{
"created": "Thu, 6 Jul 2023 17:42:56 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Shi",
"Yao",
""
],
[
"Zhang",
"Xiaofeng",
""
],
[
"zhang",
"Ran",
""
],
[
"Yang",
"Zhou",
""
],
[
"Tang",
"Xiao",
""
],
[
"Ye",
"Hongni",
""
],
[
"Wu",
"Yi",
""
]
] | [
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] |
2307.03166 | Liangzhe Yuan | Liangzhe Yuan, Nitesh Bharadwaj Gundavarapu, Long Zhao, Hao Zhou, Yin
Cui, Lu Jiang, Xuan Yang, Menglin Jia, Tobias Weyand, Luke Friedman, Mikhail
Sirotenko, Huisheng Wang, Florian Schroff, Hartwig Adam, Ming-Hsuan Yang,
Ting Liu, Boqing Gong | VideoGLUE: Video General Understanding Evaluation of Foundation Models | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We evaluate existing foundation models video understanding capabilities using
a carefully designed experiment protocol consisting of three hallmark tasks
(action recognition, temporal localization, and spatiotemporal localization),
eight datasets well received by the community, and four adaptation methods
tailoring a foundation model (FM) for a downstream task. Moreover, we propose a
scalar VideoGLUE score (VGS) to measure an FMs efficacy and efficiency when
adapting to general video understanding tasks. Our main findings are as
follows. First, task-specialized models significantly outperform the six FMs
studied in this work, in sharp contrast to what FMs have achieved in natural
language and image understanding. Second,video-native FMs, whose pretraining
data contains the video modality, are generally better than image-native FMs in
classifying motion-rich videos, localizing actions in time, and understanding a
video of more than one action. Third, the video-native FMs can perform well on
video tasks under light adaptations to downstream tasks(e.g., freezing the FM
backbones), while image-native FMs win in full end-to-end finetuning. The first
two observations reveal the need and tremendous opportunities to conduct
research on video-focused FMs, and the last confirms that both tasks and
adaptation methods matter when it comes to the evaluation of FMs.
| [
{
"created": "Thu, 6 Jul 2023 17:47:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yuan",
"Liangzhe",
""
],
[
"Gundavarapu",
"Nitesh Bharadwaj",
""
],
[
"Zhao",
"Long",
""
],
[
"Zhou",
"Hao",
""
],
[
"Cui",
"Yin",
""
],
[
"Jiang",
"Lu",
""
],
[
"Yang",
"Xuan",
""
],
[
"Jia",
"Menglin",
""
],
[
"Weyand",
"Tobias",
""
],
[
"Friedman",
"Luke",
""
],
[
"Sirotenko",
"Mikhail",
""
],
[
"Wang",
"Huisheng",
""
],
[
"Schroff",
"Florian",
""
],
[
"Adam",
"Hartwig",
""
],
[
"Yang",
"Ming-Hsuan",
""
],
[
"Liu",
"Ting",
""
],
[
"Gong",
"Boqing",
""
]
] | [
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] |
2307.03167 | Thomas Lew | Thomas Lew, Riccardo Bonalli, Marco Pavone | Risk-Averse Trajectory Optimization via Sample Average Approximation | null | null | null | null | cs.RO cs.SY eess.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Trajectory optimization under uncertainty underpins a wide range of
applications in robotics. However, existing methods are limited in terms of
reasoning about sources of epistemic and aleatoric uncertainty, space and time
correlations, nonlinear dynamics, and non-convex constraints. In this work, we
first introduce a continuous-time planning formulation with an
average-value-at-risk constraint over the entire planning horizon. Then, we
propose a sample-based approximation that unlocks an efficient,
general-purpose, and time-consistent algorithm for risk-averse trajectory
optimization. We prove that the method is asymptotically optimal and derive
finite-sample error bounds. Simulations demonstrate the high speed and
reliability of the approach on problems with stochasticity in nonlinear
dynamics, obstacle fields, interactions, and terrain parameters.
| [
{
"created": "Thu, 6 Jul 2023 17:48:27 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lew",
"Thomas",
""
],
[
"Bonalli",
"Riccardo",
""
],
[
"Pavone",
"Marco",
""
]
] | [
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] | Risk-Averse Trajectory Optimization via Sample Average Approximation | https://arxiv.org/abs/2307.03167v1 | https://arxiv.org/pdf/2307.03167v1.pdf | https://github.com/stanfordasl/riskaversetrajopt | true | true | https://paperswithcode.com/paper/risk-averse-trajectory-optimization-via | [
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] |
2307.03170 | Szymon Tworkowski | Szymon Tworkowski, Konrad Staniszewski, Miko{\l}aj Pacek, Yuhuai Wu,
Henryk Michalewski, Piotr Mi{\l}o\'s | Focused Transformer: Contrastive Training for Context Scaling | null | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models have an exceptional capability to incorporate new
information in a contextual manner. However, the full potential of such an
approach is often restrained due to a limitation in the effective context
length. One solution to this issue is to endow an attention layer with access
to an external memory, which comprises of (key, value) pairs. Yet, as the
number of documents increases, the proportion of relevant keys to irrelevant
ones decreases, leading the model to focus more on the irrelevant keys. We
identify a significant challenge, dubbed the distraction issue, where keys
linked to different semantic values might overlap, making them hard to
distinguish. To tackle this problem, we introduce the Focused Transformer
(FoT), a technique that employs a training process inspired by contrastive
learning. This novel approach enhances the structure of the (key, value) space,
enabling an extension of the context length. Our method allows for fine-tuning
pre-existing, large-scale models to lengthen their effective context. This is
demonstrated by our fine-tuning of $3B$ and $7B$ OpenLLaMA checkpoints. The
resulting models, which we name LongLLaMA, exhibit advancements in tasks
requiring a long context. We further illustrate that our LongLLaMA models
adeptly manage a $256 k$ context length for passkey retrieval.
| [
{
"created": "Thu, 6 Jul 2023 17:52:10 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tworkowski",
"Szymon",
""
],
[
"Staniszewski",
"Konrad",
""
],
[
"Pacek",
"Mikołaj",
""
],
[
"Wu",
"Yuhuai",
""
],
[
"Michalewski",
"Henryk",
""
],
[
"Miłoś",
"Piotr",
""
]
] | [
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2307.03171 | Rahul Mihir Patel | Rahul Patel, Elias B. Khalil | LEO: Learning Efficient Orderings for Multiobjective Binary Decision
Diagrams | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Approaches based on Binary decision diagrams (BDDs) have recently achieved
state-of-the-art results for multiobjective integer programming problems. The
variable ordering used in constructing BDDs can have a significant impact on
their size and on the quality of bounds derived from relaxed or restricted BDDs
for single-objective optimization problems. We first showcase a similar impact
of variable ordering on the Pareto frontier (PF) enumeration time for the
multiobjective knapsack problem, suggesting the need for deriving variable
ordering methods that improve the scalability of the multiobjective BDD
approach. To that end, we derive a novel parameter configuration space based on
variable scoring functions which are linear in a small set of interpretable and
easy-to-compute variable features. We show how the configuration space can be
efficiently explored using black-box optimization, circumventing the curse of
dimensionality (in the number of variables and objectives), and finding good
orderings that reduce the PF enumeration time. However, black-box optimization
approaches incur a computational overhead that outweighs the reduction in time
due to good variable ordering. To alleviate this issue, we propose LEO, a
supervised learning approach for finding efficient variable orderings that
reduce the enumeration time. Experiments on benchmark sets from the knapsack
problem with 3-7 objectives and up to 80 variables show that LEO is ~30-300%
and ~10-200% faster at PF enumeration than common ordering strategies and
algorithm configuration. Our code and instances are available at
https://github.com/khalil-research/leo.
| [
{
"created": "Thu, 6 Jul 2023 17:52:29 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Patel",
"Rahul",
""
],
[
"Khalil",
"Elias B.",
""
]
] | [
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] | LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams | https://arxiv.org/abs/2307.03171v1 | https://arxiv.org/pdf/2307.03171v1.pdf | https://github.com/khalil-research/leo | true | true | https://paperswithcode.com/paper/leo-learning-efficient-orderings-for | [
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] |
2307.03172 | Nelson F. Liu | Nelson F. Liu and Kevin Lin and John Hewitt and Ashwin Paranjape and
Michele Bevilacqua and Fabio Petroni and Percy Liang | Lost in the Middle: How Language Models Use Long Contexts | 15 pages, 17 figures | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While recent language models have the ability to take long contexts as input,
relatively little is known about how well the language models use longer
context. We analyze language model performance on two tasks that require
identifying relevant information within their input contexts: multi-document
question answering and key-value retrieval. We find that performance is often
highest when relevant information occurs at the beginning or end of the input
context, and significantly degrades when models must access relevant
information in the middle of long contexts. Furthermore, performance
substantially decreases as the input context grows longer, even for explicitly
long-context models. Our analysis provides a better understanding of how
language models use their input context and provides new evaluation protocols
for future long-context models.
| [
{
"created": "Thu, 6 Jul 2023 17:54:11 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Liu",
"Nelson F.",
""
],
[
"Lin",
"Kevin",
""
],
[
"Hewitt",
"John",
""
],
[
"Paranjape",
"Ashwin",
""
],
[
"Bevilacqua",
"Michele",
""
],
[
"Petroni",
"Fabio",
""
],
[
"Liang",
"Percy",
""
]
] | [
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] |
2307.03175 | Xiaoyu Zhang | Xiaoyu Zhang, Saurabh Gupta | Push Past Green: Learning to Look Behind Plant Foliage by Moving It | for project website with video, see
https://sites.google.com/view/pushpastgreen/ | null | null | null | cs.RO cs.AI cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous agriculture applications (e.g., inspection, phenotyping, plucking
fruits) require manipulating the plant foliage to look behind the leaves and
the branches. Partial visibility, extreme clutter, thin structures, and unknown
geometry and dynamics for plants make such manipulation challenging. We tackle
these challenges through data-driven methods. We use self-supervision to train
SRPNet, a neural network that predicts what space is revealed on execution of a
candidate action on a given plant. We use SRPNet with the cross-entropy method
to predict actions that are effective at revealing space beneath plant foliage.
Furthermore, as SRPNet does not just predict how much space is revealed but
also where it is revealed, we can execute a sequence of actions that
incrementally reveal more and more space beneath the plant foliage. We
experiment with a synthetic (vines) and a real plant (Dracaena) on a physical
test-bed across 5 settings including 2 settings that test generalization to
novel plant configurations. Our experiments reveal the effectiveness of our
overall method, PPG, over a competitive hand-crafted exploration method, and
the effectiveness of SRPNet over a hand-crafted dynamics model and relevant
ablations.
| [
{
"created": "Thu, 6 Jul 2023 17:55:28 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhang",
"Xiaoyu",
""
],
[
"Gupta",
"Saurabh",
""
]
] | [
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2307.03176 | Benjamin Ruben | Benjamin S. Ruben, Cengiz Pehlevan | Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles | null | null | null | null | stat.ML cond-mat.dis-nn cs.LG q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | Feature bagging is a well-established ensembling method which aims to reduce
prediction variance by training estimators in an ensemble on random subsamples
or projections of features. Typically, ensembles are chosen to be homogeneous,
in the sense the the number of feature dimensions available to an estimator is
uniform across the ensemble. Here, we introduce heterogeneous feature
ensembling, with estimators built on varying number of feature dimensions, and
consider its performance in a linear regression setting. We study an ensemble
of linear predictors, each fit using ridge regression on a subset of the
available features. We allow the number of features included in these subsets
to vary. Using the replica trick from statistical physics, we derive learning
curves for ridge ensembles with deterministic linear masks. We obtain explicit
expressions for the learning curves in the case of equicorrelated data with an
isotropic feature noise. Using the derived expressions, we investigate the
effect of subsampling and ensembling, finding sharp transitions in the optimal
ensembling strategy in the parameter space of noise level, data correlations,
and data-task alignment. Finally, we suggest variable-dimension feature bagging
as a strategy to mitigate double descent for robust machine learning in
practice.
| [
{
"created": "Thu, 6 Jul 2023 17:56:06 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ruben",
"Benjamin S.",
""
],
[
"Pehlevan",
"Cengiz",
""
]
] | [
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] | Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles | https://arxiv.org/abs/2307.03176v1 | https://arxiv.org/pdf/2307.03176v1.pdf | https://github.com/benruben87/Learning-Curves-for-Heterogeneous-Feature-Subsampled-Ridge-Ensembles | true | false | https://paperswithcode.com/paper/learning-curves-for-heterogeneous-feature | [
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] |
2307.03177 | Tianhao Wu | Tianhao Wu, Chuanxia Zheng, Tat-Jen Cham | IPO-LDM: Depth-aided 360-degree Indoor RGB Panorama Outpainting via
Latent Diffusion Model | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating complete 360-degree panoramas from narrow field of view images is
ongoing research as omnidirectional RGB data is not readily available. Existing
GAN-based approaches face some barriers to achieving higher quality output, and
have poor generalization performance over different mask types. In this paper,
we present our 360-degree indoor RGB panorama outpainting model using latent
diffusion models (LDM), called IPO-LDM. We introduce a new bi-modal latent
diffusion structure that utilizes both RGB and depth panoramic data during
training, but works surprisingly well to outpaint normal depth-free RGB images
during inference. We further propose a novel technique of introducing
progressive camera rotations during each diffusion denoising step, which leads
to substantial improvement in achieving panorama wraparound consistency.
Results show that our IPO-LDM not only significantly outperforms
state-of-the-art methods on RGB panorama outpainting, but can also produce
multiple and diverse well-structured results for different types of masks.
| [
{
"created": "Thu, 6 Jul 2023 17:57:02 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wu",
"Tianhao",
""
],
[
"Zheng",
"Chuanxia",
""
],
[
"Cham",
"Tat-Jen",
""
]
] | [
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2307.03181 | Krishnamurthy Iyer | Krishnamurthy Iyer and Haifeng Xu and You Zu | Markov Persuasion Processes with Endogenous Agent Beliefs | null | null | null | null | cs.GT econ.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a dynamic Bayesian persuasion setting where a single long-lived
sender persuades a stream of ``short-lived'' agents (receivers) by sharing
information about a payoff-relevant state. The state transitions are Markovian
and the sender seeks to maximize the long-run average reward by committing to a
(possibly history-dependent) signaling mechanism. While most previous studies
of Markov persuasion consider exogenous agent beliefs that are independent of
the chain, we study a more natural variant with endogenous agent beliefs that
depend on the chain's realized history. A key challenge to analyze such
settings is to model the agents' partial knowledge about the history
information. We analyze a Markov persuasion process (MPP) under various
information models that differ in the amount of information the receivers have
about the history of the process. Specifically, we formulate a general
partial-information model where each receiver observes the history with an
$\ell$ period lag. Our technical contribution start with analyzing two
benchmark models, i.e., the full-history information model and the no-history
information model. We establish an ordering of the sender's payoff as a
function of the informativeness of agent's information model (with no-history
as the least informative), and develop efficient algorithms to compute optimal
solutions for these two benchmarks. For general $\ell$, we present the
technical challenges in finding an optimal signaling mechanism, where even
determining the right dependency on the history becomes difficult. To bypass
the difficulties, we use a robustness framework to design a "simple"
\emph{history-independent} signaling mechanism that approximately achieves
optimal payoff when $\ell$ is reasonably large.
| [
{
"created": "Thu, 6 Jul 2023 17:58:01 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Iyer",
"Krishnamurthy",
""
],
[
"Xu",
"Haifeng",
""
],
[
"Zu",
"You",
""
]
] | [
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2307.03183 | Yuan Gong | Yuan Gong, Sameer Khurana, Leonid Karlinsky, and James Glass | Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong
General Audio Event Taggers | Accepted at Interspeech 2023. Code at
https://github.com/yuangongnd/whisper-at | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | In this paper, we focus on Whisper, a recent automatic speech recognition
model trained with a massive 680k hour labeled speech corpus recorded in
diverse conditions. We first show an interesting finding that while Whisper is
very robust against real-world background sounds (e.g., music), its audio
representation is actually not noise-invariant, but is instead highly
correlated to non-speech sounds, indicating that Whisper recognizes speech
conditioned on the noise type. With this finding, we build a unified audio
tagging and speech recognition model Whisper-AT by freezing the backbone of
Whisper, and training a lightweight audio tagging model on top of it. With <1%
extra computational cost, Whisper-AT can recognize audio events, in addition to
spoken text, in a single forward pass.
| [
{
"created": "Thu, 6 Jul 2023 17:58:28 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Gong",
"Yuan",
""
],
[
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""
],
[
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"Leonid",
""
],
[
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""
]
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] | Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers | https://arxiv.org/abs/2307.03183v1 | https://arxiv.org/pdf/2307.03183v1.pdf | https://github.com/YuanGongND/whisper-at | true | true | https://paperswithcode.com/paper/whisper-at-noise-robust-automatic-speech | [
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] |
2307.03186 | Idan Shenfeld | Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal | TGRL: An Algorithm for Teacher Guided Reinforcement Learning | null | ICML 2023 | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Learning from rewards (i.e., reinforcement learning or RL) and learning to
imitate a teacher (i.e., teacher-student learning) are two established
approaches for solving sequential decision-making problems. To combine the
benefits of these different forms of learning, it is common to train a policy
to maximize a combination of reinforcement and teacher-student learning
objectives. However, without a principled method to balance these objectives,
prior work used heuristics and problem-specific hyperparameter searches to
balance the two objectives. We present a $\textit{principled}$ approach, along
with an approximate implementation for $\textit{dynamically}$ and
$\textit{automatically}$ balancing when to follow the teacher and when to use
rewards. The main idea is to adjust the importance of teacher supervision by
comparing the agent's performance to the counterfactual scenario of the agent
learning without teacher supervision and only from rewards. If using teacher
supervision improves performance, the importance of teacher supervision is
increased and otherwise it is decreased. Our method, $\textit{Teacher Guided
Reinforcement Learning}$ (TGRL), outperforms strong baselines across diverse
domains without hyper-parameter tuning.
| [
{
"created": "Thu, 6 Jul 2023 17:58:40 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Shenfeld",
"Idan",
""
],
[
"Hong",
"Zhang-Wei",
""
],
[
"Tamar",
"Aviv",
""
],
[
"Agrawal",
"Pulkit",
""
]
] | [
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2307.03188 | Andrea Carosso | Andrea Carosso | Simulating Nelsonian Quantum Field Theory | null | null | null | null | physics.hist-ph hep-lat quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe the picture of physical processes suggested by Edward Nelson's
stochastic mechanics when generalized to quantum field theory, after an
introductory review of his theory applied to the hydrogen atom. By performing
numerical simulations of the relevant stochastic processes, we observe that
Nelson's theory provides an intuitive account of how particles can arise from
an underlying field ``beable'' -- to use a phrase of John S. Bell -- in the
case of free field theory, regularized on a lattice. We then argue that this
picture looks qualitatively similar when generalized to interacting scalar
field theory. Lastly, we compare the Nelsonian framework to various other
proposed ontologies for QFT, and remark upon their relative merits in light of
the effective field theory paradigm.
| [
{
"created": "Thu, 6 Jul 2023 17:58:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Carosso",
"Andrea",
""
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] |
2307.03190 | Aniruddha Mahapatra | Aniruddha Mahapatra, Aliaksandr Siarohin, Hsin-Ying Lee, Sergey
Tulyakov, Jun-Yan Zhu | Synthesizing Artistic Cinemagraphs from Text | null | null | null | null | cs.CV cs.GR cs.LG | http://creativecommons.org/licenses/by/4.0/ | We introduce Artistic Cinemagraph, a fully automated method for creating
cinemagraphs from text descriptions - an especially challenging task when
prompts feature imaginary elements and artistic styles, given the complexity of
interpreting the semantics and motions of these images. Existing single-image
animation methods fall short on artistic inputs, and recent text-based video
methods frequently introduce temporal inconsistencies, struggling to keep
certain regions static. To address these challenges, we propose an idea of
synthesizing image twins from a single text prompt - a pair of an artistic
image and its pixel-aligned corresponding natural-looking twin. While the
artistic image depicts the style and appearance detailed in our text prompt,
the realistic counterpart greatly simplifies layout and motion analysis.
Leveraging existing natural image and video datasets, we can accurately segment
the realistic image and predict plausible motion given the semantic
information. The predicted motion can then be transferred to the artistic image
to create the final cinemagraph. Our method outperforms existing approaches in
creating cinemagraphs for natural landscapes as well as artistic and
other-worldly scenes, as validated by automated metrics and user studies.
Finally, we demonstrate two extensions: animating existing paintings and
controlling motion directions using text.
| [
{
"created": "Thu, 6 Jul 2023 17:59:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Mahapatra",
"Aniruddha",
""
],
[
"Siarohin",
"Aliaksandr",
""
],
[
"Lee",
"Hsin-Ying",
""
],
[
"Tulyakov",
"Sergey",
""
],
[
"Zhu",
"Jun-Yan",
""
]
] | [
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] |
2307.03192 | Akika Nakamichi | Akika Nakamichi, Manaya Matsui, Masahiro Morikawa | Seismic 1/f Fluctuations from Amplitude Modulated Earth's Free
Oscillation | 6 pages, 6 figures | null | null | null | physics.geo-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We first report that the seismic data from around the world yield 1/f
fluctuations if we exclude giant earthquakes. We investigate the origin of this
1/f fluctuation based on our recent proposal: 1/f noise is amplitude modulation
and demodulation. We speculate that this amplitude modulation is encoded by the
resonance with the Earth Free Oscillation (EFO) and demodulated by fault
rupture. We partially demonstrate this scenario by analyzing the EFO
eigenmodes. We briefly describe possible verifications of our theory of 1/f
fluctuations based on EFO.
| [
{
"created": "Sun, 18 Jun 2023 12:42:45 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Nakamichi",
"Akika",
""
],
[
"Matsui",
"Manaya",
""
],
[
"Morikawa",
"Masahiro",
""
]
] | [
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