Papers made digestable

2023-02-02

- Detailed ablations also reveal the mechanism of our proposal
- It brings meaningful new challenges to the community
- Codes, data, and models are available at https://github.com/ucaszyp/STEPS.

Authors: Yupeng Zheng, Chengliang Zhong, Pengfei Li, Huan-ang Gao, Yuhang Zheng, Bu Jin, Ling Wang, Hao Zhao, Guyue Zhou, Qichao Zhang, Dongbin Zhao.

2023-02-02

- We establish strong PAC and regret lower bounds for learning in revealing POMDPs
- Technically, our hard instance construction adapts techniques in \emph{distribution testing}, which is new to the RL literature and may be of independent interest.

Authors: Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai.

2023-02-02

- We propose the first Bayesian encoder for metric learning
- We actualize this by first proving that the contrastive loss is a valid log-posterior
- We then propose three methods that ensure a positive definite Hessian
- Lastly, we present a novel decomposition of the Generalized Gauss-Newton approximation.

Authors: Frederik Warburg, Marco Miani, Silas Brack, Soren Hauberg.

2023-02-02

- We present evidence for a suppressed growth rate of large-scale structure during the dark-energy dominated era
- When combined, they yield $\gamma=0.633^{+0.025}_{-0.024}$, excluding $\gamma=0.55$ at a statistical significance of 3.7$\sigma$.

Authors: Nhat-Minh Nguyen, Dragan Huterer, Yuewei Wen.

2023-02-02

- Our framework is learned from in-the-wild 2D image collections only, without any 3D annotations
- The height field represents the surface elevation of 3D scenes, while the semantic field provides detailed scene semantics
- Lastly, a neural volumetric renderer, learned from 2D image collections through adversarial training, is employed to produce photorealistic images
- Extensive experiments demonstrate the effectiveness of SceneDreamer and superiority over state-of-the-art methods in generating vivid yet diverse unbounded 3D worlds.

Authors: Zhaoxi Chen, Guangcong Wang, Ziwei Liu.

2023-02-02

- The basic idea behind it is to treat a given device as a black box that given some input generates an output, and then to verify whether it works as expected by only studying the statistics generated by this device
- The resource required in most of these certification schemes is quantum non-locality.

Authors: Shubhayan Sarkar.

2023-02-02

- We propose Randomized Greedy Learning (RGL) algorithm and theoretically prove that it achieves a $\frac{1}{2}$-regret upper bound of $\tilde{\mathcal{O}}(n T^{\frac{2}{3}})$ for horizon $T$ and number of arms $n$
- We also show in experiments that RGL empirically outperforms other full-bandit variants in submodular and non-submodular settings.

We investigate the problem of unconstrained combinatorial multi-armed bandits
with full-bandit feedback and stochastic rewards for submodular maximization.
Previous works investigate the same problem assuming a submodular and monotone
reward function. In this work, we study a more general problem, i.e., when the
reward function is not necessarily monotone, and the submodularity is assumed
only in expectation. We propose Randomized Greedy Learning (RGL) algorithm and
theoretically prove that it achieves a $\frac{1}{2}$-regret upper bound of
$\tilde{\mathcal{O}}(n T^{\frac{2}{3}})$ for horizon $T$ and number of arms
$n$. We also show in experiments that RGL empirically outperforms other
full-bandit variants in submodular and non-submodular settings.

Authors: Fares Fourati, Vaneet Aggarwal, Christopher John Quinn, Mohamed-Slim Alouini.

2023-02-02

- The discrete values of the binary rate set a maximum measurable intensity.

Authors: Lucas J. Koerner.

2023-02-02

- We map the calculation into a Riemann-Hilbert problem with a piecewise constant matrix for a doubly connected domain
- We find an explicit solution for $\alpha=2$ and an implicit one for $\alpha>2$.

We consider the R\'enyi-$\alpha$ tripartite information $I_3^{(\alpha)}$ of
three adjacent subsystems in the stationary state emerging after global
quenches in noninteracting spin chains from both homogeneous and bipartite
states. We identify settings in which $I_3^{(\alpha)}$ remains nonzero also in
the limit of infinite lengths and develop a field theory description. We map
the calculation into a Riemann-Hilbert problem with a piecewise constant matrix
for a doubly connected domain. We find an explicit solution for $\alpha=2$ and
an implicit one for $\alpha>2$. In the latter case, we develop a rapidly
convergent perturbation theory that we use to derive analytic formulae
approximating $I_3^{(\alpha)}$ with outstanding accuracy.

Authors: Vanja Marić, Maurizio Fagotti.

2023-02-02

- The collective radiation spectrum from a leptonic collision is derived
- Ultrashort, ultrabright, and high-luminosity colliding gamma-ray beams are generated
- The theoretical results are confirmed by self-consistent 3-dimensional QED particle-in-cell simulations.

Authors: W. L. Zhang, T. Grismayer, L. O. Silva.

2023-02-02

- Given a complete Riemannian manifold $\mathcal{M}\subset\mathbb{R}^d$ which is a Lipschitz neighbourhood retract of dimension $m+n$, of class $C^{3,\beta}$, without boundary and an oriented, closed submanifold $\Gamma \subset \mathcal M$ of dimension $m-1$, of class $C^{3,\alpha}$ with $\alpha<\beta$, which is a boundary in integral homology, we construct a complete metric space $\mathcal{B}$ of $C^{3,\alpha}$-perturbations of $\Gamma$ inside $\mathcal{M}$ with the following property
- We deduce that the typical element $b\in\mathcal{B}$ admits a unique solution to the Plateau problem.

Given a complete Riemannian manifold $\mathcal{M}\subset\mathbb{R}^d$ which
is a Lipschitz neighbourhood retract of dimension $m+n$, of class
$C^{3,\beta}$, without boundary and an oriented, closed submanifold $\Gamma
\subset \mathcal M$ of dimension $m-1$, of class $C^{3,\alpha}$ with
$\alpha<\beta$, which is a boundary in integral homology, we construct a
complete metric space $\mathcal{B}$ of $C^{3,\alpha}$-perturbations of $\Gamma$
inside $\mathcal{M}$ with the following property. For the typical element
$b\in\mathcal B$, in the sense of Baire categories, every $m$-dimensional
integral current in $\mathcal{M}$ which solves the corresponding Plateau
problem has an open dense set of boundary points with density $1/2$. We deduce
that the typical element $b\in\mathcal{B}$ admits a unique solution to the
Plateau problem. Moreover we prove that, in a complete metric space of integral
currents without boundary in $\mathbb{R}^{m+n}$, metrized by the flat norm, the
typical boundary admits a unique solution to the Plateau problem.

Authors: Gianmarco Caldini, Andrea Marchese, Andrea Merlo, Simone Steinbrüchel.

2023-02-02

- We provide a complete classification, up to order-isomorphism, of all possible Wadge hierarchies on zero-dimensional Polish spaces using (essentially) countable ordinals as complete invariants
- All results are based on a complete and explicit description of the Wadge hierarchy on an arbitrary zero-dimensional Polish space, depending on its topological properties.

Authors: Raphaël Carroy, Luca Motto Ros, Salvatore Scamperti.

2023-02-02

- We present speculative sampling, an algorithm for accelerating transformer decoding by enabling the generation of multiple tokens from each transformer call
- This is combined with a novel modified rejection sampling scheme which preserves the distribution of the target model within hardware numerics.

We present speculative sampling, an algorithm for accelerating transformer
decoding by enabling the generation of multiple tokens from each transformer
call. Our algorithm relies on the observation that the latency of parallel
scoring of short continuations, generated by a faster but less powerful draft
model, is comparable to that of sampling a single token from the larger target
model. This is combined with a novel modified rejection sampling scheme which
preserves the distribution of the target model within hardware numerics. We
benchmark speculative sampling with Chinchilla, a 70 billion parameter language
model, achieving a 2-2.5x decoding speedup in a distributed setup, without
compromising the sample quality or making modifications to the model itself.

Authors: Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, John Jumper.

2023-02-02

- The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors
- CERN's Large Hadron Collider (LHC) represents a huge step forward in this quest
- The primary science goal is to search for physics beyond the SM and, should it be discovered, to study its implications
- Both NSF and DOE are making large detector upgrade investments so the HL-LHC can operate in this high-rate environment.

Authors: Brian Bockelman, Peter Elmer, Gordon Watts.

2023-02-02

- We consider both the standard diffusion models, e.g., DDPM, and the text-to-image diffusion models, e.g., Stable Diffusion
- Experimental results demonstrate that our methods precisely infer the membership with high confidence on both of the two scenarios across six different datasets

Diffusion-based generative models have shown great potential for image
synthesis, but there is a lack of research on the security and privacy risks
they may pose. In this paper, we investigate the vulnerability of diffusion
models to Membership Inference Attacks (MIAs), a common privacy concern. Our
results indicate that existing MIAs designed for GANs or VAE are largely
ineffective on diffusion models, either due to inapplicable scenarios (e.g.,
requiring the discriminator of GANs) or inappropriate assumptions (e.g., closer
distances between synthetic images and member images). To address this gap, we
propose Step-wise Error Comparing Membership Inference (SecMI), a black-box MIA
that infers memberships by assessing the matching of forward process posterior
estimation at each timestep. SecMI follows the common overfitting assumption in
MIA where member samples normally have smaller estimation errors, compared with
hold-out samples. We consider both the standard diffusion models, e.g., DDPM,
and the text-to-image diffusion models, e.g., Stable Diffusion. Experimental
results demonstrate that our methods precisely infer the membership with high
confidence on both of the two scenarios across six different datasets

Authors: Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu.