nils-herrmann/clean_scirepeval huggingface dataset:
Double Q-learning: New Analysis and Sharper Finite-time Bound
Authors: Lin Zhao, Huaqing Xiong, Yingbin Liang, Wei Zhang

Double Q-learning \citep{hasselt2010double} has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning. However, theoretical understanding of double Q-learning is rather limited and the only existing finite-time analysis was recently established in } under a polynomial learning rate. This paper analyzes the more challenging case with a rescaled linear/constant learning rate for which the previous method does not appear to be applicable. We develop new analytical tools that achieve an order-level better finite-time convergence rate than the previously established result. Specifically, we show that synchronous double Q-learning attains an $\epsilon$-accurate global optimum with a time complexity of $\Omega\left(\frac{\ln D}{(1-\gamma)^7\epsilon^2}  \right)$, and the asynchronous algorithm attains a time complexity of $\tilde{\Omega}\left(\frac{L}{(1-\gamma)^7\epsilon^2} \right)$, where $D$ is the cardinality of the state-action space, $\gamma$ is the discount factor, and $L$ is a parameter related to the sampling strategy for asynchronous double Q-learning. These results improve the order-level dependence of the convergence rate on all major parameters $(\epsilon,1-\gamma, D, L)$ provided in . The new analysis in this paper presents a more direct and succinct approach for characterizing the finite-time convergence rate of double Q-learning.
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
Authors: Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei

Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information. Conceptually, the representational information provides guidance for the model to construct better node features; while the correlational information indicates the correlation between node outcomes conditional on node features. Through a simulation study, we find that many popular GNN models are incapable of effectively utilizing the correlational information. By leveraging the idea of the copula, a principled way to describe the dependence among multivariate random variables, we offer a general solution. The proposed Copula Graph Neural Network (CopulaGNN) can take a wide range of GNN models as base models and utilize both representational and correlational information stored in the graphs. Experimental results on two types of regression tasks verify the effectiveness of the proposed method.
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes
Authors: David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J Bekkers, Mark Hoogendoorn, Jan van Gemert

When designing Convolutional Neural Networks (CNNs), one must select the size of the convolutional kernels before training. Recent works show CNNs benefit from different kernel sizes at different layers, but exploring all possible combinations is unfeasible in practice. A more efficient approach is to learn the kernel size during training. However, existing works that learn the kernel size have a limited bandwidth. These approaches scale kernels by dilation, and thus the detail they can describe is limited. In this work, we propose FlexConv, a novel convolutional operation with which high bandwidth convolutional kernels of learnable kernel size can be learned at a fixed parameter cost. FlexNets model long-term dependencies without the use of pooling, achieve state-of-the-art performance on several sequential datasets, outperform recent works with learned kernel sizes, and are competitive with much deeper ResNets on image benchmark datasets. Additionally, FlexNets can be deployed at higher resolutions than those seen during training. To avoid aliasing, we propose a novel kernel parameterization with which the frequency of the kernels can be analytically controlled. Our novel kernel parameterization shows higher descriptive power and faster convergence speed than existing parameterizations. This leads to important improvements in classification accuracy.
Domain-Independent Dominance of Adaptive Methods
Authors: Pedro Savarese, David McAllester, Sudarshan Babu, Michael Maire

From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. In light of this observation, we demonstrate that, against conventional wisdom, Adam can also outperform SGD on vision tasks, as long as the coupling between its learning rate and adaptability is taken into account. In practice, AvaGrad matches the best results, as measured by generalization accuracy, delivered by any existing optimizer (SGD or adaptive) across image classification (CIFAR, ImageNet) and character-level language modelling (Penn Treebank) tasks. This later observation, alongside of AvaGrad's decoupling of hyperparameters, could make it the preferred optimizer for deep learning, replacing both SGD and Adam.
Evaluations and Methods for Explanation through Robustness Analysis
Authors: Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh

Among multiple ways of interpreting a machine learning model, measuring the importance of a set of features tied to a prediction is probably one of the most intuitive way to explain a model. In this paper, we establish the link between a set of features to a prediction with a new evaluation criteria, robustness analysis, which measures the minimum tolerance of adversarial perturbation. By measuring the tolerance level for an adversarial attack, we can extract a set of features that provides most robust support for a current prediction, and also can extract a set of features that contrasts the current prediction to a target class by setting a targeted adversarial attack. By applying this methodology to various prediction tasks across multiple domains, we observed the derived explanations are indeed capturing the significant feature set qualitatively and quantitatively.
UPS: optimizing Undirected Positive Sparse graph for neural graph filtering
Authors: Mikhail Yurochkin, Dung Thai, Hung Hai Bui, XuanLong Nguyen

In this work we propose a novel approach for learning graph representation of the data using gradients obtained via backpropagation. Next we build a neural network architecture compatible with our optimization approach and motivated by graph filtering in the vertex domain. We demonstrate that the learned graph has richer structure than often used nearest neighbors graphs constructed based on features similarity. Our experiments demonstrate that we can improve prediction quality for several convolution on graphs architectures, while others appeared to be insensitive to the input graph.
How to Adapt Your Large-Scale Vision-and-Language Model
Authors: Konwoo Kim, Michael Laskin, Igor Mordatch, Deepak Pathak

Pre-training large-scale vision and language models (e.g. CLIP) has shown promising results in representation and transfer learning. We investigate the question of how to efficiently adapt these models to downstream tasks. For image classification, linear probes have been the standard for ease of use and efficiency, while for language, other approaches like prompt tuning have emerged. We analyze several fine-tuning methods across a diverse set of image classification tasks across two spectra investigating the amount and similarity of downstream data to that of pretraining one. We find that just tuning LayerNorm parameters is a surprisingly effective baseline across the board. We further demonstrate a simple yet effective strategy that combines LayerNorm-tuning with general fine-tuning methods to improve their performance and benchmark them on few-shot adaption and distribution shift tasks. Finally, we provide an empirical analysis and recommend general recipes for efficient transfer learning of vision and language models. Website at https://sites.google.com/view/adapt-large-scale-models
Model Comparison of Beer data classification using an electronic nose
Authors: Mohammed Abdi, Aminat Adebiyi, Andrea Fasoli, Alberto Mannari, Ronald Labby, Luisa Bozano

Olfaction has been and still is an area which is challenging to the research community. Like other senses of the body, there has been a push to replicate the sense of smell to aid in identifying odorous compounds in the form of an electronic nose. At IBM, our team (Cogniscent) has designed a modular sensor board platform based on the artificial olfaction concept we called EVA (Electronic Volatile Analyzer). EVA is an IoT electronic nose device that aims to reproduce olfaction in living begins by integrating an array of partially specific and uniquely selective smell recognition sensors which are directly exposed to the target chemical analyte or the environment. We are exploring a new technique called temperature-controlled oscillation, which gives us virtual array of sensors to represent our signals/ fingerprint. In our study, we run experiments on identifying different types of beers using EVA. In order to successfully carry this classification task, the entire process starting from preparation of samples, having a consistent protocol of data collection in place all the way to providing the data to be analyzed and input to a machine learning model is very important. On this paper, we will discuss the process of sniffing volatile organic compounds from liquid beer samples and successfully classifying different brands of beers as a pilot test. We researched on different machine learning models in order to get the best classification accuracy for our Beer samples. The best classification accuracy is achieved by using a multi-level perceptron (MLP) artificial neural network (ANN) model, classification of three different brands of beers after splitting one-week data to a training and testing set yielded an accuracy of 97.334. While using separate weeks of data for training and testing set the model yielded an accuracy of 67.812, this is because of drift playing a role in the overall classification process. Using Random forest, the classification accuracy achieved by the model is 0.923. And Decision Tree achieved 0.911.
Improving Neural Network Generalization via Promoting Within-Layer Diversity
Authors: Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj

Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage the diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. By penalizing similarities and promoting diversity, we encourage each unit within the layer to learn a distinctive representation and, thus, to enrich the data representation learned and to increase the total capacity of the model. We theoretically study how the within-layer activation diversity affects the generalization performance of a neural network and prove that increasing the diversity of hidden activations reduces the estimation error. In addition to the theoretical guarantees, we present an extensive empirical study confirming that the proposed approach enhances the performance of state-of-the-art neural network models and decreases the generalization gap in multiple tasks.
DDSP: Differentiable Digital Signal Processing
Authors: Jesse Engel, Lamtharn (Hanoi) Hantrakul, Chenjie Gu, Adam Roberts

Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully incorporates strong domain knowledge of signal processing and perception, but has been less actively researched due to limited expressivity and difficulty integrating with modern auto-differentiation-based machine learning methods. In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods. Focusing on audio synthesis, we achieve high-fidelity generation without the need for large autoregressive models or adversarial losses, demonstrating that DDSP enables utilizing strong inductive biases without losing the expressive power of neural networks. Further, we show that combining interpretable modules permits manipulation of each separate model component, with applications such as independent control of pitch and loudness, realistic extrapolation to pitches not seen during training, blind dereverberation of room acoustics, transfer of extracted room acoustics to new environments, and transformation of timbre between disparate sources. In short, DDSP enables an interpretable and modular approach to generative modeling, without sacrificing the benefits of deep learning. The library will is available at https://github.com/magenta/ddsp and we encourage further contributions from the community and domain experts.
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
Authors: Avery Ma, Aladin Virmaux, Kevin Scaman, Juwei Lu

Do all adversarial examples have the same consequences? An autonomous driving system misclassifying a pedestrian as a car may induce a far more dangerous --and even potentially lethal-- behavior than, for instance, a car as a bus. In order to better tackle this important problematic, we introduce the concept of hierarchical adversarial robustness. Given a dataset whose classes can be grouped into coarse-level labels, we define hierarchical adversarial examples as the ones leading to a misclassification at the coarse level. To improve the resistance of neural networks to hierarchical attacks, we introduce a hierarchical adversarially robust (HAR) network design that decomposes a single classification task into one coarse and multiple fine classification tasks, before being specifically trained by adversarial defense techniques. As an alternative to an end-to-end learning approach, we show that HAR significantly improves the robustness of the network against $\ell_{\infty}$  and $\ell_{2}$bounded hierarchical attacks on CIFAR-100.
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation
Authors: Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang, Balázs Kégl

Unsupervised domain adaptation (UDA) aims at exploiting related but different data sources in order to tackle a common task in a target domain. UDA remains a central yet challenging problem in machine learning.
    In this paper, we present an approach based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians.
    Our Knothe-Rosenblatt Domain Adaptation (KRDA) then takes advantage of the triangularity of the autoregressive models to build an explicit mapping of the source samples into the target domain. We show that the transfer map built by KRDA preserves each component quantiles of the observations, hence aligning the representations of the different data sets in the same target domain.
    Finally, we show that KRDA has state-of-the-art performance on both synthetic and real world UDA problems.
Kokoyi: Executable LaTeX for End-to-end Deep Learning
Authors: Minjie Wang, Haoming Lu, Yu Gai, Lesheng Jin, Zihao Ye, Zheng Zhang

Despite substantial efforts from the deep learning system community to relieve researchers and practitioners from the burden of implementing models with ever-growing complexity, a considerable lingual gap remains between developing models in the language of mathematics and implementing them in the languages of computer. The mission of Kokoyi is to close this gap by enabling automatic translation of mathematics into efficient implementations, thereby making math-in-codes and math-in-model consistent. This paper presents our first step towards the goal: kokoyi-lang, a programming language with the syntax of LaTeX and the semantics of deep learning mathematics, and a prototype kokoyi-lang compiler and runtime supporting advanced optimizations such as auto-batching. Kokoyi is integrated with Jupyter Notebook, and will be released in open-source.
Imitation Learning from Pixel Observations for Continuous Control
Authors: Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, Denis Yarats

We study imitation learning using only visual observations for controlling dynamical systems with continuous states and actions. This setting is attractive due to the large amount of video data available from which agents could learn from. However, it is challenging due to $i)$ not observing the actions and $ii)$ the high-dimensional visual space. In this setting, we explore recipes for imitation learning based on adversarial learning and optimal transport. A key feature of our methods is to use representations from the RL encoder to compute imitation rewards. These recipes enable us to scale these methods to attain expert-level performance on visual continuous control tasks in the DeepMind control suite. We investigate the tradeoffs of these approaches and present a comprehensive evaluation of the key design choices. To encourage reproducible research in this area, we provide an easy-to-use implementation for benchmarking visual imitation learning, including our methods.
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Authors: Haibo Yang, Minghong Fang, Jia Liu

Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data.  FL has received increasing attention in recent years thanks to its data privacy protection, communication efficiency and a linear speedup for convergence in training (i.e., convergence performance increases linearly with respect to the number of workers). However, existing studies on linear speedup for convergence are only limited to the assumptions of i.i.d. datasets across workers and/or full worker participation, both of which rarely hold in practice. So far, it remains an open question whether or not the linear speedup for convergence is achievable under non-i.i.d. datasets with partial worker participation in FL.  In this paper, we show that the answer is affirmative.  Specifically, we show that the federated averaging (FedAvg) algorithm (with two-sided learning rates) on non-i.i.d. datasets in non-convex settings achieves a convergence rate $\mathcal{O}(\frac{1}{\sqrt{mKT}} + \frac{1}{T})$ for full worker participation and a convergence rate $\mathcal{O}(\frac{\sqrt{K}}{\sqrt{nT}} + \frac{1}{T})$ for partial worker participation, where $K$ is the number of local steps, $T$ is the number of total communication rounds, $m$ is the total worker number and $n$ is the worker number in one communication round if for partial worker participation. Our results also reveal that the local steps in FL could help the convergence and show that the maximum number of local steps can be improved to $T/m$ in full worker participation. We conduct extensive experiments on MNIST and CIFAR-10 to verify our theoretical results.
Graph-Based Continual Learning
Authors: Binh Tang, David S. Matteson

Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an array of independent memory slots. In this work, we propose to augment such an array with a learnable random graph that captures pairwise similarities between its samples, and use it not only to learn new tasks but also to guard against forgetting. Empirical results on several benchmark datasets show that our model consistently outperforms recently proposed baselines for task-free continual learning.
Scalable Robust Federated Learning with Provable Security Guarantees
Authors: Andrew Liu, Jacky Y. Zhang, Nishant Kumar, Dakshita Khurana, Oluwasanmi O Koyejo

Federated averaging, the most popular aggregation approach in federated learning, is known to be vulnerable to failures and adversarial updates from clients that wish to disrupt training. While median aggregation remains one of the most popular alternatives to improve training robustness, the naive combination of median and secure multi-party computation (MPC) is unscalable. To this end, we propose an efficient approximate median aggregation with MPC privacy guarantees on the multi-silo setting, e.g., across hospitals, with two semi-honest non-colluding servers. The proposed method protects the confidentiality of client gradient updates against both semi-honest clients and servers. Asymptotically, the cost of our approach scales only linearly with the number of clients, whereas the naive MPC median scales quadratically. Moreover, we prove that the convergence of the proposed federated learning method is robust to a wide range of failures and attacks. Empirically, we show that our method inherits the robustness properties of the median while converging faster than the naive MPC median for even a small number of clients.
MelNet: A Generative Model for Audio in the Frequency Domain
Authors: Sean Vasquez, Mike Lewis

Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps.  While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms.  By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales which time-domain models have yet to achieve.  We demonstrate that our model captures longer-range dependencies than time-domain models such as WaveNet across a diverse set of unconditional generation tasks, including single-speaker speech generation, multi-speaker speech generation, and music generation.