nils-herrmann/clean_scirepeval huggingface dataset:
Emergent Coordination Through Competition
Authors: Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.
Gradient Based Memory Editing for Task-Free Continual Learning
Authors: Xisen Jin, Junyi Du, Xiang Ren

Prior work on continual learning often operate in a “task-aware” manner, by assuming that the task boundaries and identifies of the data examples are known at all times. While in practice, it is rarely the case that such information are exposed to the methods (i.e., thus called “task-free”)–a setting that is relatively underexplored. Recent attempts on task-free continual learning build on previous memory replay methods and focus on developing memory construction and replay strategies such that model performance over previously seen examples can be best retained. In this paper, looking from a complementary angle, we propose a novel approach to “edit” memory examples so that the edited memory can better retain past performance when they are replayed. We use gradient updates to edit memory examples so that they are more likely to be “forgotten” in the future. Experiments on five benchmark datasets show the proposed method can be seamlessly combined with baselines to significantly improve the performance.
A Spectral Perspective on Deep Supervised Community Detection
Authors: Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the numerical performances of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to Euclidean graph (e.g., images),  high-frequencies are less crucial to community detection. In particular, it is possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies: this is surprising because contrary to GCNs, no cascade of filtering along the graph structure is involved and it indicates that the important spectral components for the supervised community detection task are essentially in the low-frequency domain.
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win
Authors: Utku Evci, Yani Ioannou, Cem Keskin, Yann Dauphin

Sparse Neural Networks (NNs) can match the generalization of dense NNs using a fraction of the compute/storage for inference, and also have the potential to enable efficient training. However, naively training unstructured sparse NNs from random initialization results in significantly worse generalization, with the notable exception of Lottery Tickets (LTs) and Dynamic Sparse Training (DST). In this work, we attempt to answer: (1) why training unstructured sparse networks from random initialization performs poorly and; and (2) what makes LTs and DST the exceptions? We show that sparse NNs have poor gradient flow at initialization and propose a modified initialization for unstructured connectivity. Furthermore, we find that DST methods significantly improve gradient flow during training over traditional sparse training methods. Finally, we show that LTs do not improve gradient flow, rather their success lies in re-learning the pruning solution they are derived from — however, this comes at the cost of learning novel solutions.
Convergence Analysis of Homotopy-SGD for Non-Convex Optimization
Authors: Matilde Gargiani, Andrea Zanelli, Moritz Diehl, Quoc Tran-Dinh, Frank Hutter

First-order stochastic methods for solving large-scale non-convex optimization problems are widely used in many big-data applications, e.g. training deep neural networks as well as other complex and potentially non-convex machine learning
models. Their inexpensive iterations generally come together with slow global convergence rate (mostly sublinear), leading to the necessity of carrying out a very high number of iterations before the iterates reach a neighborhood of a minimizer. In this work, we present a first-order stochastic algorithm based on a combination of homotopy methods and SGD, called Homotopy-Stochastic Gradient Descent (H-SGD), which finds interesting connections with some proposed heuristics in the literature, e.g. optimization by Gaussian continuation, training by diffusion, mollifying networks. Under some mild and realistic assumptions on the problem structure, we conduct a theoretical analysis of the proposed algorithm. Our analysis shows that, with a specifically designed scheme for the homotopy parameter, H-SGD enjoys a global linear rate of convergence to a neighborhood of a minimizer while maintaining fast and inexpensive iterations. Experimental evaluations confirm the theoretical results and show that H-SGD can outperform standard SGD.
Lie-Access Neural Turing Machines
Authors: Greg Yang, Alexander Rush

External neural memory structures have recently become a popular tool for
  algorithmic deep learning
  (Graves et al. 2014; Weston et al. 2014).  These models
  generally utilize differentiable versions of traditional discrete
  memory-access structures (random access, stacks, tapes) to provide
  the storage necessary for computational tasks.  In
  this work, we argue that these neural memory systems lack specific
  structure important for relative indexing, and propose an
  alternative model, Lie-access memory, that is explicitly designed
  for the neural setting.  In this paradigm, memory is accessed using
  a continuous head in a key-space manifold. The head is moved via Lie
  group actions, such as shifts or rotations, generated by a
  controller, and memory access is performed by linear smoothing in
  key space. We argue that Lie groups provide a natural generalization
  of discrete memory structures, such as Turing machines, as they
  provide inverse and identity operators while maintaining
  differentiability. To experiment with this approach, we implement
  a simplified Lie-access neural Turing machine (LANTM) with
  different Lie groups.  We find that this approach is able to perform
  well on a range of algorithmic tasks.
Learning Self-Imitating Diverse Policies
Authors: Tanmay Gangwani, Qiang Liu, Jian Peng

The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-making process. When rewards are only sparsely available during an episode, or a rewarding feedback is provided only after episode termination, these algorithms perform sub-optimally due to the difficultly in credit assignment. Alternatively, trajectory-based policy optimization methods, such as cross-entropy method and evolution strategies, do not require per-timestep rewards, but have been found to suffer from high sample complexity by completing forgoing the temporal nature of the problem. Improving the efficiency of RL algorithms in real-world problems with sparse or episodic rewards is therefore a pressing need. In this work, we introduce a self-imitation learning algorithm that exploits and explores well in the sparse and episodic reward settings. We view each policy as a state-action visitation distribution and formulate policy optimization as a divergence minimization problem. We show that with Jensen-Shannon divergence, this divergence minimization problem can be reduced into a policy-gradient algorithm with shaped rewards learned from experience replays. Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards, and significantly better in environments with sparse and episodic rewards. We then discuss limitations of self-imitation learning, and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies. We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks.
A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks
Authors: Changhee Lee, Mihaela van der Schaar

Currently available survival analysis methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks. This paper develops a novel deep learning architecture that flexibly incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions for one or multiple competing risk(s). Unlike existing works in the survival analysis on the basis of longitudinal data, the proposed method learns the time-to-event distributions without specifying underlying stochastic assumptions of the longitudinal or the time-to-event processes. Thus, our method is able to learn associations between the longitudinal data and the various associated risks in a fully data-driven fashion. We demonstrate the power of our method by applying it to real-world longitudinal datasets and show a drastic improvement over state-of-the-art methods in discriminative performance. Furthermore, our analysis of the variable importance and dynamic survival predictions will yield a better understanding of the predicted risks which will result in more effective health care.
Learning Manifold Patch-Based Representations of Man-Made Shapes
Authors: Dmitriy Smirnov, Mikhail Bessmeltsev, Justin Solomon

Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications. Focusing on piecewise-smooth man-made shapes, we propose a new representation that is usable in conventional CAD modeling pipelines and can also be learned by deep neural networks. We demonstrate its benefits by applying it to the task of sketch-based modeling. Given a raster image, our system infers a set of parametric surfaces that realize the input in 3D. To capture piecewise smooth geometry, we learn a special shape representation: a deformable parametric template composed of Coons patches. Naively training such a system, however, is hampered by non-manifold artifacts in the parametric shapes and by a lack of data. To address this, we introduce loss functions that bias the network to output non-self-intersecting shapes and implement them as part of a fully self-supervised system, automatically generating both shape templates and synthetic training data. We develop a testbed for sketch-based modeling, demonstrate shape interpolation, and provide comparison to related work.
Automated Relational Meta-learning
Authors: Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li

In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.
Generative Adversarial Self-Imitation Learning
Authors: Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee

This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework. Instead of directly maximizing rewards, GASIL focuses on reproducing past good trajectories, which can potentially make long-term credit assignment easier when rewards are sparse and delayed. GASIL can be easily combined with any policy gradient objective by using GASIL as a learned reward shaping function. Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
Authors: Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.
To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space.
This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty.
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.
Modeling Latent Attention Within Neural Networks
Authors: Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L.S. Wong, Michael L. Littman

Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned mechanisms that contribute to such effective behaviors and more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision and natural language processing. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.
Revisiting Loss Modelling for Unstructured Pruning
Authors: César Laurent, Camille Ballas, Thomas George, Pascal Vincent, Nicolas Ballas

By removing parameters from deep neural networks, unstructured pruning methods aim at cutting down memory footprint and computational cost, while maintaining prediction accuracy. In order to tackle this otherwise intractable problem, many of these methods model the loss landscape using first or second order Taylor expansions to identify which parameters can be discarded. We revisit loss modelling for unstructured pruning: we show the importance of ensuring locality of the pruning steps, and systematically compare first and second order Taylor expansions. Finally, we show that better preserving the original network function does not necessarily transfer to better performing networks after fine-tuning, suggesting that only considering the impact of pruning on the loss might not be a sufficient objective to design good pruning criteria.
Discovery of Predictive Representations With a Network of General Value Functions
Authors: Matthew Schlegel, Andrew Patterson, Adam White, Martha White

The ability of an agent to {\em discover} its own learning objectives has long been considered a key ingredient for artificial general intelligence. Breakthroughs in autonomous decision making and reinforcement learning have primarily been in domains where the agent's goal is outlined and clear: such as playing a game to win, or driving safely. Several studies have demonstrated that learning extramural sub-tasks and auxiliary predictions can improve (1) single human-specified task learning, (2) transfer of learning, (3) and the agent's learned representation of the world. In all these examples, the agent was instructed what to learn about. We investigate a framework for discovery: curating a large collection of predictions, which are used to construct the agent's representation of the world. Specifically, our system maintains a large collection of predictions, continually pruning and replacing predictions. We highlight the importance of considering stability rather than convergence for such a system, and develop an adaptive, regularized algorithm towards that aim. We provide several experiments in computational micro-worlds demonstrating that this simple approach can be effective for discovering useful predictions autonomously.
Rotograd: Dynamic Gradient Homogenization for Multitask Learning
Authors: Adrián Javaloy, Isabel Valera

GradNorm (Chen et al., 2018) is a broadly used gradient-based approach for training multitask networks, where different tasks share, and thus compete during learning, for the network parameters. GradNorm eases the fitting of all individual tasks by dynamically equalizing the contribution of each task to the overall gradient magnitude. However, it does not prevent the individual tasks’ gradients from conflicting, i.e., pointing towards opposite directions, and thus resulting in a poor multitask performance. In this work we propose Rotograd, an extension to GradNorm that addresses this problem by dynamically homogenizing not only the gradient magnitudes but also their directions across tasks. For this purpose,Rotograd adds a layer of task-specific rotation matrices that aligns all the task gradients. Importantly, we then analyze Rotograd (and its predecessor) through the lens of game theory, providing theoretical guarantees on the algorithm stability and convergence. Finally, our experiments on several real-world datasets and network architectures show that Rotograd outperforms previous approaches for multitask learning.
Generative Negative Replay for Continual Learning
Authors: Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo Lomonaco

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-world problems. One of the most effective strategies to control catastrophic forgetting, the Achilles’ heel of continual learning, is storing part of the old data and replay them interleaved with new experiences (also known as the replay approach). Generative replay, that is using generative models to provide replay patterns on demand, is particularly intriguing, however, it was shown to be effective mainly under simplified assumptions, such as simple scenarios and low-dimensional benchmarks. 
In this paper, we show that, while the generated data are usually not able to improve the classification accuracy for the old classes, they can be effective as negative examples (or antagonists) to learn the new classes, especially when the learning experiences are small and contain examples of just one or few classes. The proposed approach is validated on complex class-incremental and data-incremental continual learning scenarios (CORe50 and ImageNet-1000) composed of high-dimensional data and a large number of training experiences: a setup where existing generative replay approaches usually fail.
SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model
Authors: Hideaki Hayashi, Seiichi Uchida

In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability. Nevertheless, it is difficult to determine the number of components. We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM). In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning. This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components. The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner. Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity. Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.