nils-herrmann/clean_scirepeval huggingface dataset:
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
Authors: Andrea Zunino, Sarah Adel Bargal, Pietro Morerio, Jianming Zhang, Stan Sclaroff, Vittorio Murino

We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction: the firing of neurons in specific paths. In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout. In essence, we dropout with higher probability those neurons which contribute more to decision making at training time. This approach penalizes high saliency neurons that are most relevant for model prediction, i.e. those having stronger evidence. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization, resulting in a plasticity-like behavior, a characteristic of human brains too. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression using several metrics over four image/video recognition benchmarks.
Gaussian Conditional Random Fields for Classification
Authors: Andrija Petrovic, Mladen Nikolic, Milos Jovanovic, Boris Delibasic

In this paper, a Gaussian conditional random field model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient, and open to improvements previously applied to GCRF regression. Two different forms of the algorithm are presented: GCRFBCb (GCRGBC - Bayesian) and GCRFBCnb (GCRFBC - non-Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton-Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. It was shown that both models achieve better prediction performance than relevant baselines. Advantages and disadvantages of the proposed models are discussed.
Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks
Authors: Yijie Guo, Jongwook Choi, Marcin Moczulski, Samy Bengio, Mohammad Norouzi, Honglak Lee

Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards. However, it is very difficult to achieve similar success without relying on expert demonstrations. Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior. To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks. We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards. Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima. In particular, we report a state-of-the-art score of more than 20,000 points on Montezumas Revenge without using expert demonstrations or resetting to arbitrary states.
Transformational Sparse Coding
Authors: Dimitrios C. Gklezakos, Rajesh P. N. Rao

A fundamental problem faced by object recognition systems is that
objects and their features can appear in different locations, scales
and orientations. Current deep learning methods attempt to achieve
invariance to local translations via pooling, discarding the locations
of features in the process.  Other approaches explicitly learn
transformed versions of the same feature, leading to representations
that quickly explode in size. Instead of discarding the rich and
useful information about feature transformations to achieve
invariance, we argue that models should learn object features
conjointly with their transformations to achieve equivariance.  We
propose a new model of unsupervised learning based on sparse coding
that can learn object features jointly with their affine
transformations directly from images. Results based on learning from
natural images indicate that our approach
matches the reconstruction quality of traditional sparse coding but
with significantly fewer degrees of freedom while simultaneously
learning transformations from data. These results open the door to
scaling up unsupervised learning to allow deep feature+transformation
learning in a manner consistent with the ventral+dorsal stream
architecture of the primate visual cortex.
Disentangled cyclic reconstruction for domain adaptation
Authors: David Bertoin, Emmanuel Rachelson

The domain adaptation problem involves learning a unique classification or regression model capable of performing on both a source and a target domain. Although the labels for the source data are available during training, the labels in the target domain are unknown. An effective way to tackle this problem lies in extracting insightful features invariant to the source and target domains. In this work, we propose splitting the information for each domain into a task-related representation and its complimentary context representation. We propose an original method to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation problem. In particular, our method allows disentanglement in the target domain, despite the absence of training labels. This enables the isolation of task-specific information from both domains and a projection into a common representation. The task-specific representation allows efficient transfer of knowledge acquired from the source domain to the target domain. We validate the proposed method on several classical domain adaptation benchmarks and illustrate the benefits of disentanglement for domain adaptation.
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Authors: Ziyi Yang, Chenguang Zhu, Michael Zeng, Xuedong Huang, Eric Darve

Text summarization aims to extract essential information from a piece of text and transform it into a concise version. Existing unsupervised abstractive summarization models use recurrent neural networks framework and ignore abundant unlabeled corpora resources. In order to address these issues, we propose TED, a transformer-based unsupervised summarization system with dataset-agnostic pretraining. We first leverage the lead bias in news articles to pretrain the model on large-scale corpora. Then, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are abstractive and containing even higher proportions of novel tokens than those from supervised models.
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
Authors: Hongyao Tang, Zhaopeng Meng, Jianye HAO, Chen Chen, Daniel Graves, Dong Li, Wulong Liu, Yaodong Yang

The value function lies in the heart of Reinforcement Learning (RL), which defines the long-term evaluation of a policy in a given state. In this paper, we propose Policy-extended Value Function Approximator (PeVFA) which extends the conventional value to be not only a function of state but also an explicit policy representation. Such an extension enables PeVFA to preserve values of multiple policies in contrast to a conventional one with limited capacity for only one policy, inducing the new characteristic of \emph{value generalization among policies}. From both the theoretical and empirical lens, we study value generalization along the policy improvement path (called local generalization), from which we derive a new form of Generalized Policy Iteration with PeVFA to improve the conventional learning process. Besides, we propose a framework to learn the representation of an RL policy,  studying several different approaches to learn an effective policy representation from policy network parameters and state-action pairs through contrastive learning and action prediction. In our experiments, Proximal Policy Optimization (PPO) with PeVFA significantly outperforms its vanilla counterpart in MuJoCo continuous control tasks, demonstrating the effectiveness of value generalization offered by PeVFA and policy representation learning.
Superbloom: Bloom filter meets Transformer
Authors: John Anderson, Qingqing Huang, Walid Krichene, Steffen Rendle, Li Zhang

We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash tokens in a much smaller space, similarly to a Bloom filter. We show that by applying a multi-layer Transformer to these Bloom filter digests, we are able to obtain models with high accuracy. They outperform models of a similar size without hashing and, to a large degree, models of a much larger size trained using sampled softmax with the same computational budget. Our key observation is that it is important to use a multi-layer Transformer for Bloom filter digests to remove ambiguity in the hashed input. We believe this provides an alternative method to solving problems with large vocabulary size.
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers.
Authors: Ali Ghobadzadeh, Deepak Sridhar, Juwei Lu, Wei Li

Deep learning models owe their success at large, to the availability of a large amount of annotated data. They try to extract features from the data that contain useful information needed to improve their performance on target applications. Most works focus on directly optimizing the target loss functions to improve the accuracy by allowing the model to implicitly learn representations from the data. There has not been much work on using background/noise data to estimate the statistics of in-domain data to improve the feature representation of deep neural networks. In this paper, we probe this direction by deriving a relationship between the estimation of unknown parameters of the probability density function (pdf) of input data and classification accuracy. Using this relationship, we show that having a better estimate of the unknown parameters using background and in-domain data provides better features which leads to better accuracy. Based on this result, we introduce a simple but effective detection booster training (DBT) method that applies a detection loss function on the early layers of a neural network to discriminate in-domain data points from noise/background data, to improve the classifier accuracy. The background/noise data comes from the same family of pdfs of input data but with different parameter sets (e.g., mean, variance). In addition, we also show that our proposed DBT method improves the accuracy even with limited labeled in-domain training samples as compared to normal training. We conduct experiments on face recognition, image classification, and speaker classification problems and show that our method achieves superior performance over strong baselines across various datasets and model architectures.
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Authors: Sebastian Caldas, Jakub Konečný, Brendan McMahan, Ameet Talwalkar

Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent server-to-client; and (2) Federated Dropout, which allows users to efficiently train locally on smaller subsets of the global model and also provides a reduction in both client-to-server communication and local computation. We empirically show that these strategies, combined with existing compression approaches for client-to-server communication, collectively provide up to a 9.6x reduction in server-to-client communication, a 1.5x reduction in local computation, and a 24x reduction in upload communication, all without degrading the quality of the final model. We thus comprehensively reduce FL's impact on client device resources, allowing higher capacity models to be trained, and a more diverse set of users to be reached.
Understanding Generalized Label Smoothing when Learning with Noisy Labels
Authors: Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Yang Liu

Label smoothing (LS) is an arising learning paradigm that uses the positively weighted average of both the hard training labels and uniformly distributed soft labels. It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model. Later it was reported LS even helps with improving robustness when learning with noisy labels. However, we observe that the advantage of LS vanishes when we operate in a high label noise regime. Puzzled by the observation, we proceeded to discover that several proposed learning-with-noisy-labels solutions in the literature instead relate more closely to $\textit{negative label smoothing}$ (NLS), which defines as using a negative weight to combine the hard and soft labels! We show that NLS differs substantially from LS in their achieved model confidence. To differentiate the two cases, we will call LS the positive label smoothing (PLS), and this paper unifies PLS and NLS into $\textit{generalized label smoothing}$ (GLS). We provide understandings for the properties of GLS when learning with noisy labels. Among other established properties, we theoretically show NLS is considered more beneficial when the label noise rates are high. We provide extensive experimental results on multiple benchmarks to support our findings too.
What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
Authors: Gaurav Malhotra, Jeffrey Bowers

Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition. This has lead to the speculation that both systems use similar mechanisms to perform recognition. In this study, we conducted a series of simulations that indicate that there is a fundamental difference between human vision and CNNs: while object recognition in humans relies on analysing shape, CNNs do not have such a shape-bias. We teased apart the type of features selected by the model by modifying the CIFAR-10 dataset so that, in addition to containing objects with shape, the images concurrently contained non-shape features, such as a noise-like mask. When trained on these modified set of images, the model did not show any bias towards selecting shapes as features. Instead it relied on whichever feature allowed it to perform the best prediction -- even when this feature was a noise-like mask or a single predictive pixel amongst 50176 pixels. We also found that regularisation methods, such as batch normalisation or Dropout, did not change this behaviour and neither did past or concurrent experience with images from other datasets.
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness
Authors: Nina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang

We formally define a feature-space attack where the adversary can perturb datapoints by arbitrary amounts but in restricted directions. By restricting the attack to a small random subspace, our model provides a clean abstraction for non-Lipschitz networks which map small input movements to large feature movements. We prove that classifiers with the ability to abstain are provably more powerful than those that cannot in this setting. Specifically, we show that no matter how well-behaved the natural data is, any classifier that cannot abstain will be defeated by such an adversary.  However, by allowing abstention, we give a parameterized algorithm with provably good performance against such an adversary when classes are reasonably well-separated in feature space and the dimension of the feature space is high. We further use a data-driven method to set our algorithm parameters to optimize over the accuracy vs. abstention trade-off with strong theoretical guarantees. Our theory has direct applications to the technique of contrastive learning, where we empirically demonstrate the ability of our algorithms to obtain high robust accuracy with only small amounts of abstention in both supervised and self-supervised settings. Our results provide a first formal abstention-based gap, and a first provable optimization for the induced trade-off in an adversarial defense setting.
Reusing Preprocessing Data as Auxiliary Supervision in Conversational Analysis
Authors: Joshua Yee Kim, Kalina Yacef

Conversational analysis systems are trained using noisy human labels and often require heavy preprocessing during multi-modal feature extraction. Using noisy labels in single-task learning increases the risk of over-fitting. However, auxiliary tasks could improve the performance of the primary task learning. This approach is known as Primary Multi-Task Learning (MTL). A challenge of MTL is the selection of beneficial auxiliary tasks that avoid negative transfer. In this paper, we explore how the preprocessed data used for feature engineering can be re-used as auxiliary tasks in Primary MTL, thereby promoting the productive use of data in the form of auxiliary supervision learning. Our main contributions are: (1) the identification of sixteen beneficially auxiliary tasks, (2) the method of distributing learning capacity between the primary and auxiliary tasks, and (3) the relative supervision hierarchy between the primary and auxiliary tasks. Extensive experiments on IEMOCAP and SEMAINE data validate the improvements over single-task approaches, and suggest that it may generalize across multiple primary tasks.
Mint: Matrix-Interleaving for Multi-Task Learning
Authors: Tianhe Yu, Saurabh Kumar, Eric Mitchell, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn

Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data. Applications of neural networks often consider learning in the context of a single task. However, in many scenarios what we hope to learn is not just a single task, but a model that can be used to solve multiple different tasks. Such multi-task learning settings have the potential to improve data efficiency and generalization by sharing data and representations across tasks. However, in some challenging multi-task learning settings, particularly in reinforcement learning, it is very difficult to learn a single model that can solve all the tasks while realizing data efficiency and performance benefits. Learning each of the tasks independently from scratch can actually perform better in such settings, but it does not benefit from the representation sharing that multi-task learning can potentially provide. In this work, we develop an approach that endows a single model with the ability to represent both extremes: joint training and independent training. To this end, we introduce matrix-interleaving (Mint), a modification to standard neural network models that projects the activations for each task into a different learned subspace, represented by a per-task and per-layer matrix. By learning these matrices jointly with the other model parameters, the optimizer itself can decide how much to share representations between tasks. On three challenging multi-task supervised learning and reinforcement learning problems with varying degrees of shared task structure, we find that this model consistently matches or outperforms joint training and independent training, combining the best elements of both.
Learning Axioms to Compute Verifiable Symbolic Expression Equivalence Proofs Using Graph-to-Sequence Networks
Authors: Steven James Kommrusch, Louis-Noel Pouchet, Theo Barolett

We target the problem of proving the semantic equivalence between two complex expressions represented as typed trees, and demonstrate our system on expressions from a rich multi-type symbolic language for linear algebra. We propose the first graph-to-sequence deep learning system to generate axiomatic proofs of equivalence between program pairs. We generate expressions which include scalars, vectors and matrices and 16 distinct operators combining them, with 147 distinct axioms of equivalence. We study the robustness of the system to generate proofs of increasing length, demonstrating how incremental graph-to-sequence networks can learn to represent complex and verifiable symbolic reasoning. It achieves 93% average true positive coverage on 10,000 test cases while ensuring zero false positives by design.
State-Regularized Recurrent Networks
Authors: Cheng Wang, Mathias Niepert

Recurrent networks are a widely used class of neural architectures.  They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN's state transition dynamics, and (b) forces RNNs to operate more like automata with external memory and less like finite state machines.
COLA: Consistent Learning with Opponent-Learning Awareness
Authors: Timon Willi, Johannes Treutlein, Alistair Letcher, Jakob Nicolaus Foerster

Optimization problems with multiple, interdependent losses, such as Generative Adversarial Networks (GANs) or multi-agent RL, are commonly formalized as differentiable games. 
Learning with Opponent-Learning Awareness (LOLA) introduced opponent shaping to this setting. More specifically, LOLA introduced an augmented learning rule that accounts for the agent's influence on the anticipated learning step of the other agents. However, the original LOLA formulation is inconsistent because LOLA models other agents as naive learners rather than LOLA agents. 
In previous work, this inconsistency was stated to be the root cause of LOLA's failure to preserve stable fixed points (SFPs). We provide a counterexample by investigating cases where Higher-Order LOLA (HOLA) converges. 
Furthermore, we show that, contrary to claims made, Competitive Gradient Descent (CGD) does not solve the consistency problem.
Next, we propose a new method called Consistent LOLA (COLA), which learns update functions that are consistent under mutual opponent shaping. Lastly, we empirically compare the performance and consistency of HOLA, LOLA, and COLA on a set of general-sum learning games.