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d7167114 | We present a variational approximation to the information bottleneck ofTishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method "Deep Variational Information Bottleneck", or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack. | DEEP VARIATIONAL INFORMATION BOTTLENECK |
d252781139 | The brain effortlessly extracts latent causes of stimuli, but how it does this at the network level remains unknown. Most prior attempts at this problem proposed neural networks that implement independent component analysis, which works under the limitation that latent causes are mutually independent. Here, we relax this limitation and propose a biologically plausible neural network that extracts correlated latent sources by exploiting information about their domains. To derive this network, we choose the maximum correlative information transfer from inputs to outputs as the separation objective under the constraint that the output vectors are restricted to the set where the source vectors are assumed to be located. The online formulation of this optimization problem naturally leads to neural networks with local learning rules. Our framework incorporates infinitely many set choices for the source domain and flexibly models complex latent structures. Choices of simplex or polytopic source domains result in networks with piecewise-linear activation functions. We provide numerical examples to demonstrate the superior correlated source separation capability for both synthetic and natural sources. | CORRELATIVE INFORMATION MAXIMIZATION BASED BIOLOGICALLY PLAUSIBLE NEURAL NETWORKS FOR CORRELATED SOURCE SEPARATION |
d13757156 | Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between random, wide, fully connected, feedforward networks with more than one hidden layer and Gaussian processes with a recursive kernel definition. We show that, under broad conditions, as we make the architecture increasingly wide, the implied random function converges in distribution to a Gaussian process, formalising and extending existing results byNeal (1996)to deep networks. To evaluate convergence rates empirically, we use maximum mean discrepancy. We then compare finite Bayesian deep networks from the literature to Gaussian processes in terms of the key predictive quantities of interest, finding that in some cases the agreement can be very close. We discuss the desirability of Gaussian process behaviour and review non-Gaussian alternative models from the literature. 1 | Gaussian Process Behaviour in Wide Deep Neural Networks |
d256416154 | Monocular Depth Estimation (MDE) is a critical component in applications suchas autonomous driving. There are various attacks against MDE networks. These attacks, especially the physical ones, pose a great threat to the security of such systems. Traditional adversarial training method requires ground-truth labels hence cannot be directly applied to self-supervised MDE that does not have groundtruth depth. Some self-supervised model hardening techniques (e.g., contrastive learning) ignore the domain knowledge of MDE and can hardly achieve optimal performance. In this work, we propose a novel adversarial training method for self-supervised MDE models based on view synthesis without using ground-truth depth. We improve adversarial robustness against physical-world attacks using L 0norm-bounded perturbation in training. We compare our method with supervised learning based and contrastive learning based methods that are tailored for MDE. Results on two representative MDE networks show that we achieve better robustness against various adversarial attacks with nearly no benign performance degradation. * Corresponding authors. | ADVERSARIAL TRAINING OF SELF-SUPERVISED MONOCULAR DEPTH ESTIMATION AGAINST PHYSICAL-WORLD ATTACKS |
d49208503 | Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations, our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets. | SELFLESS SEQUENTIAL LEARNING |
d202888819 | We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space. The set of rewrites (i.e. transformations) that can be successfully performed on a statement represents essential semantic features of the statement. We can compress this information by embedding the formula in a vector space, such that the vector associated with a statement can be used to predict whether a statement can be rewritten by other theorems. Predicting the embedding of a formula generated by some rewrite rule is naturally viewed as approximate reasoning in the latent space. In order to measure the effectiveness of this reasoning, we perform approximate deduction sequences in the latent space and use the resulting embedding to inform the semantic features of the corresponding formal statement (which is obtained by performing the corresponding rewrite sequence using real formulas). Our experiments show that graph neural networks can make non-trivial predictions about the rewrite-success of statements, even when they propagate predicted latent representations for several steps. Since our corpus of mathematical formulas includes a wide variety of mathematical disciplines, this experiment is a strong indicator for the feasibility of deduction in latent space in general. | Mathematical Reasoning in Latent Space |
d257771709 | Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the deviation from the behavior policy as computing Q-values using outof-distribution (OOD) actions will suffer from errors due to distributional shift. The recent proposed In-sample Learning paradigm (i.e., IQL), which improves the policy by quantile regression using only data samples, shows great promise because it learns an optimal policy without querying the value function of any unseen actions. However, it remains unclear how this type of method handles the distributional shift in learning the value function. In this work, we make a key finding that the in-sample learning paradigm arises under the Implicit Value Regularization (IVR) framework. This gives a deeper understanding of why the in-sample learning paradigm works, i.e., it applies implicit value regularization to the policy. Based on the IVR framework, we further propose two practical algorithms, Sparse Q-learning (SQL) and Exponential Q-learning (EQL), which adopt the same value regularization used in existing works, but in a complete insample manner. Compared with IQL, we find that our algorithms introduce sparsity in learning the value function, making them more robust in noisy data regimes. We also verify the effectiveness of SQL and EQL on D4RL benchmark datasets and show the benefits of in-sample learning by comparing them with CQL in small data regimes. Code is available at https://github.com/ryanxhr/IVR. or value regularization, which directly modifies the Q-function to be pessimistic (Kumar et al., 2020; Kostrikov et al., 2021a;An et al., 2021;Bai et al., 2021). Nevertheless, this imposes a trade-off between accurate value estimation (more regularization) and maximum policy performance (less regularization).In this work, we find that we could alleviate the trade-off in out-of-sample learning by performing implicit value regularization, this bypasses querying the value function of any unseen actions, allows learning an optimal policy using in-sample learning * . More specifically, we propose the Implicit Value Regulazization (IVR) framework, in which a general form of behavior regularizers is added to the policy learning objective. Because of the regularization, the optimal policy in the IVR framework has a closed-form solution, which can be expressed by imposing weight on the behavior policy. The weight can be computed by a state-value function and an action-value function, the state-value function serves as a normalization term to make the optimal policy integrate to 1. It is usually intractable to find a closed form of the state-value function, however, we make a subtle mathematical transformation and show its equivalence to solving a convex optimization problem. In this manner, both of these two value functions can be learned by only dataset samples.Note that the recently proposed method, IQL (Kostrikov et al., 2021b), although derived from a different view (i.e., approximate an upper expectile of dataset actions given a state), remains pretty close to the learning paradigm of our framework. Furthermore, our IVR framework explains why learning the state-value function is important in IQL and gives a deeper understanding of how IQL handles the distributional shift: it is doing implicit value regularization, with the hyperparameter τ to control the strength. This explains one disturbing issue of IQL, i.e., the role of τ does not have a perfect match between theory and practice. In theory, τ should be close to 1 to obtain an optimal policy while in practice a larger τ may give a worse result.1 2 E s∼D (T x V − V )(s) 2 , respectively. Note that x could be the learned policy π or the behavior policy µ, if x = µ, then a ∼ µ and a ∼ µ are equal to a ∼ D and a ∼ D, respectively. In offline RL, since D typically does not contain all possible transitions (s, a, s ), one actually uses an empirical policy evaluation operator that only backs up a single s sample, we denote this operator asT x . , et al. Rl unplugged: A suite of benchmarks for offline reinforcement learning. In Proc. of NeurIPS, 2020. Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine. Reinforcement learning with deep energy-based policies. In Proc. of ICML, pp. 1352-1361, 2017. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. | OFFLINE RL WITH NO OOD ACTIONS: IN-SAMPLE LEARNING VIA IMPLICIT VALUE REGULARIZATION |
d263829977 | Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden patterns and trends.Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with large language models (LLMs) remains an understudied problem.In this work, we perform the first comprehensive study of encoding graph-structured data as text for consumption by LLMs.We show that LLM performance on graph reasoning tasks varies on three fundamental levels:(1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered.These novel results provide valuable insight on strategies for encoding graphs as text.Using these insights we illustrate how the correct choice of encoders can boost performance on graph reasoning tasks inside LLMs by 4.8% to 61.8%, depending on the task. | TALK LIKE A GRAPH: ENCODING GRAPHS FOR LARGE LANGUAGE MODELS |
d220364453 | This paper proposes a new type of implicit generative model that is able to quickly learn a latent representation without an explicit encoder. This is achieved with an implicit neural network that takes as inputs points in the coordinate space alongside a latent vector initialised with zeros. The gradients of the data fitting loss with respect to this zero vector are jointly optimised to act as latent points that capture the data manifold. The results show similar characteristics to autoencoders, but with fewer parameters and the advantages of implicit representation networks. * Authors contributed equally. | Gradient Origin Networks |
d184486852 | Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local heuristics to search for a directly acyclic graph (DAG) according to a predefined score function. While these methods, e.g., greedy equivalence search (GES), may have attractive results with infinite samples and certain model assumptions, they are less satisfactory in practice due to finite data and possible violation of assumptions. Motivated by recent advances in neural combinatorial optimization, we propose to use reinforcement learning (RL) to search for the DAG with the best scoring. Our encoder-decoder model takes observable data as input and generates graph adjacency matrices that are used to compute corresponding rewards. The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity. In contrast with typical RL applications where the goal is to learn a policy, we use RL as a search strategy and our final output would be the graph, among all graphs generated during training, that achieves the best reward. We conduct experiments on both synthetic and real data, and show that the proposed approach not only has an improved search ability but also allows for a flexible score function under the acyclicity constraint.Preprint. Under review. | Causal Discovery with Reinforcement Learning |
d189856873 | Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound propagation (IBP) based training is efficient and significantly outperforms linear relaxation based methods on many tasks, yet it may suffer from stability issues since the bounds are much looser especially at the beginning of training. In this paper, we propose a new certified adversarial training method, CROWN-IBP, by combining the fast IBP bounds in a forward bounding pass and a tight linear relaxation based bound, CROWN, in a backward bounding pass. CROWN-IBP is computationally efficient and consistently outperforms IBP baselines on training verifiably robust neural networks. We conduct large scale experiments on MNIST and CIFAR datasets, and outperform all previous linear relaxation and bound propagation based certified defenses in ∞ robustness. Notably, we achieve 7.02% verified test error on MNIST at = 0.3, and 66.94% on CIFAR-10 with = 8/255. * Work partially done during an internship at DeepMind. . On the effectiveness of interval bound propagation for training verifiably robust models. arXiv preprint arXiv:1810.12715, 2018. Conv 4 5 × 5+1, Conv 8 5 × 5+1, Conv 8 5 × 5+4, FC 64 F Conv 8 5 × 5+1, Conv 16 5 × 5+1, Conv 16 5 × 5+4, FC 128 G Conv 4 3 × 3+1, Conv 4 4 × 4+2, Conv 8 3 × 3+1, Conv 8 4 × 4+2, FC 256, FC 256 H Conv 8 3 × 3+1, Conv 8 4 × 4+2, Conv 16 3 × 3+1, Conv 16 4 × 4+2, FC 256, FC 256 I Conv 4 3 × 3+1, Conv 4 4 × 4+2, Conv 8 3 × 3+1, Conv 8 4 × 4+2, FC 512, FC 512 J Conv 8 3 × 3+1, Conv 8 4 × 4+2, Conv 16 3 × 3+1, Conv 16 4 × 4+2, FC 512, FC 512 K Conv 16 3 × 3+1, Conv 16 4 × 4+2, Conv 32 3 × 3+1, Conv 32 4 × 4+2, FC 256, FC 256 L Conv 16 3 × 3+1, Conv 16 4 × 4+2, Conv 32 3 × 3+1, Conv 32 4 × 4+2, FC 512, FC 512 M Conv 32 3 × 3+1, Conv 32 4 × 4+2, Conv 64 3 × 3+1, Conv 64 4 × 4+2, FC 512, FC 512 N Conv 64 3 × 3+1, Conv 64 4 × 4+2, Conv 128 3 × 3+1, Conv 128 4 × 4+2, FC 512, FC 512 O(MNIST Only) Conv 64 5 × 5+1, Conv 128 5 × 5+1, Conv 128 4 × 4+4, FC 512 P(MNIST Only) Conv 32 5 × 5+1, Conv 64 5 × 5+1, Conv 64 4 × 4+4, FC 512 Q Conv 16 5 × 5+1, Conv 32 5 × 5+1, Conv 32 5 × 5+4, FC 512 R Conv 32 3 × 3+1, Conv 64 3 × 3+1, Conv 64 3 × 3+4, FC 512 S(CIFAR-10 Only) Conv 32 4 × 4+2, Conv 64 4 × 4+2, FC 128 T(CIFAR-10 Only) Conv 64 4 × 4+2, Conv 128 4 × 4+2, FC 256 | Towards Stable and Efficient Training of Verifiably Robust Neural Networks |
d210849195 | This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.Published as a conference paper at ICLR 2020 of neighbors and communication is restricted to its neighborhood, and 2) training is offline and global information is available in rollout training minibatches, despite a decentralized training process.The contributions of this paper are three-fold. First, we formulate NMARL under the aforementioned NSC assumptions as a decentralized spatiotemporal MDP, and introduce a spatial discount factor to stabilize training, especially for non-communicative algorithms. Second, we propose a new neural communication protocol, called NeurComm, to adaptively share information on both system states and agent behaviors. Third, we design and simulate realistic NMARL environments to evaluate and compare our approaches against recent MARL baselines. 1 2 RELATED WORK MARL works can be classified into four groups based on their communication methods. The first group is non-communicative and focuses on stabilizing training with advanced value estimation methods. In MADDPG, each action-value is estimated by a centralized critic based on global observations and actions (or inferred actions)(Lowe et al., 2017). COMA extends the same idea to A2C and estimates each advantage using a centralized critic and a counterfactual baseline(Foerster et al., 2018). In Dec-HDRQN (Omidshafiei et al., 2017) and PS-TRPO (Gupta et al., 2017), the centralized critic takes local observations, but the parameters are shared globally. In the NMARL work of Zhang et al.(2018), the critic is fully decentralized but each takes global observations and performs consensus updates. In this paper, we empirically confirm that a spatial discount factor helps stabilize the training of non-communicative algorithms under neighborhood observation.The second group considers heuristic communication protocols or direct information sharing. Foerster et al.(2017)shows performance gains with directly-shared low dimensional policy fingerprints from other agents. Similarly, mean field MARL takes the average of neighbor policies for informed action-value estimation(Yang et al., 2018). The major disadvantage of this group is that, unlike NeurComm, the communication is not explicitly designed for performance optimization, which may cause inefficient and redundant communications in execution.The third group proposes learnable communication protocols. In DIAL, the message is generated together with action-value estimation by each DQN agent, then it is encoded and summed with other input signals at the receiver side(Foerster et al., 2016). CommNet is a more general communication protocol, but it calculates the mean of all messages instead of encoding them(Sukhbaatar et al., 2016). Both works, especially CommNet, incur an information loss due to aggregation on input signals. Another collection of works focuses on communications in strategy games. In BiCNet (Peng et al., 2017), a bi-directional RNN is used to enable flat communication among agents, while in Master-Slave (Kong et al., 2017), two-way message passing is utilized in a hierarchical RNN architecture of master and slave agents. In contrast to existing protocols, NeurComm 1) encodes and concatenates signals, instead of aggregating them, to minimize information loss, and 2) includes policy fingerprints in communication to reduce non-stationarity.The fourth group focuses on communication attentions to selectively send messages. ATOC (Jiang & Lu, 2018) learns a soft attention which allocates a communication probability to each other agent, while IC3Net (Singh et al., 2018) learns a hard binary attention which decides communicating or not. These works are especially useful when each agent has to prioritize the communication targets. NMARL is less likely the case since the communication range is restricted to small neighborhoods.SPATIOTEMPORAL RLThis section formulates the NMARL problem as a decentralized spatiotemporal MDP, and introduces the spatial discount factor to reduce its learning difficulty. To simplify the notation, we assume the true system state is observable, and use "state" and "observation" interchangeably. This does not affect the validity of proposed methods in practice. To save space, all proofs are deferred to A. | MULTI-AGENT REINFORCEMENT LEARNING FOR NETWORKED SYSTEM CONTROL |
d2906360 | We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver. Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.Published as a conference paper at ICLR 2017 show orders-of-magnitude improvements over optimized standard search techniques and a Recurrent Neural Network-based approach to the problem. | DEEPCODER: LEARNING TO WRITE PROGRAMS |
d52898972 | By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets. This work is mainly focused on the adversarial training yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method. Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets. To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably. | IMPROVING THE GENERALIZATION OF ADVERSARIAL TRAINING WITH DOMAIN ADAPTATION |
d251320393 | When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional servergrade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of FL, fewer efforts have been devoted to accelerating on-device training. Such stage, which repeats hundreds of times (i.e. every round) and can involve thousands of devices, accounts for the majority of the time required to train federated models and, the totality of the energy consumption at the client side. In this work, we present the first study on the unique aspects that arise when introducing sparsity at training time in FL workloads. We then propose ZeroFL, a framework that relies on highly sparse operations to accelerate on-device training. Models trained with ZeroFL and 95% sparsity achieve up to 2.3% higher accuracy compared to competitive baselines obtained from adapting a state-of-the-art sparse training framework to the FL setting.Published as a conference paper at ICLR 2022 this way overall device utilization (e.g. fewer local epochs) and number of communication rounds. Other optimization techniques such as quantization and sparsity have been used in the context of FL but mostly as a way to reduce communication costs(Liu et al., 2021;Amiri et al., 2020;Shahid et al., 2021)but not to accelerate on-device training.The use of sparse operations (e.g. convolutions) at training time has recently been shown to be an effective technique to accelerate training in centralised settingsGoli & Aamodt, 2020;Raihan & Aamodt, 2020). The resulting models are as good or close to their densely-trained counterparts despite reducing by up to 90% their FLOPs budget and, resulting in an overall up to 3.3× training speedup. Acceleration is achieved by performing sparse convolutions during the forward and/or backward pass, which requires at least one of the operands (i.e. inputs, weights, gradients) to be sufficiently sparse and, software and hardware support for such operations. However, it is unclear how the different FL-specific challenges (i.e. data imbalance, stateless clients, periodic aggregation) will restrict the quality of the global model. This work considers the challenges and opportunities of inducing high levels of sparsity to accelerate training on-device for FL workloads, and provides the following contributions:• The first framework for Federated Learning that leverages sparsity as a mechanism to accelerate on-device training by inducing up to 95% sparse weights and activations. This work considers three popular datasets: CIFAR-10 and FEMNIST for image classification and, SpeechCommands for audio classification. | ZEROFL: EFFICIENT ON-DEVICE TRAINING FOR FEDERATED LEARNING WITH LOCAL SPARSITY |
d258079422 | Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain-adaptive pre-training (or continual DAP-training). Existing research has shown that further pre-training an LM using a domain corpus to adapt the LM to the domain can improve the end-task performance in the domain. This paper proposes a novel method to continually DAP-train an LM with a sequence of unlabeled domain corpora to adapt the LM to these domains to improve their endtask performances. The key novelty of our method is a soft-masking mechanism that directly controls the update to the LM. A novel proxy is also proposed to preserve the general knowledge in the original LM. Additionally, it contrasts the representations of the previously learned domain knowledge (including the general knowledge in the pre-trained LM) and the knowledge from the current full network to achieve knowledge integration. The method not only overcomes catastrophic forgetting, but also achieves knowledge transfer to improve end-task performances. Empirical evaluation demonstrates the effectiveness of the proposed method. 1 This paper focuses on continual domain-adaptive pre-training (or continual DAP-training) of LMs. It is known that DAP-training 2 an LM (without continual learning) using a large unlabeled domain corpus before end-task fine-tuning achieves better results(Gururangan et al., 2020;Ke et al., 2022b). This paper goes a step further to continually learn to improve an LM's ability to handle new or emerging domains or topics without forgetting the skills or knowledge learned in the past. This is important in the real world, where the data shifts constantly and new domains, events or topics keep emerging(Ke et al., 2022b)and the LM needs to be updated to serve the users better. | CONTINUAL PRE-TRAINING OF LANGUAGE MODELS |
d255440621 | Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted separately, and it remains unclear whether certain design patterns exist for parameter-efficient fine-tuning. Thus, we present a parameter-efficient fine-tuning design paradigm and discover design patterns that are applicable to different experimental settings. Instead of focusing on designing another individual tuning strategy, we introduce parameter-efficient fine-tuning design spaces that parameterize tuning structures and tuning strategies. Specifically, any design space is characterized by four components: layer grouping, trainable parameter allocation, tunable groups, and strategy assignment. Starting from an initial design space, we progressively refine the space based on the model quality of each design choice and make greedy selection at each stage over these four components. We discover the following design patterns: (i) group layers in a spindle pattern; (ii) allocate the number of trainable parameters to layers uniformly; (iii) tune all the groups; (iv) assign proper tuning strategies to different groups. These design patterns result in new parameter-efficient fine-tuning methods. We show experimentally that these methods consistently and significantly outperform investigated parameter-efficient fine-tuning strategies across different backbone models and different tasks in natural language processing 1 . | PARAMETER-EFFICIENT FINE-TUNING DESIGN SPACES |
d229297679 | At the heart of many robotics problems is the challenge of learning correspondences across domains. For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and the real world; transfer learning requires correspondences between different robotics environments. This paper aims to learn correspondence across domains differing in representation (vision vs. internal state), physics parameters (mass and friction), and morphology (number of limbs). Importantly, correspondences are learned using unpaired and randomly collected data from the two domains. We propose dynamics cycles that align dynamic robot behavior across two domains using a cycle-consistency constraint. Once this correspondence is found, we can directly transfer the policy trained on one domain to the other, without needing any additional fine-tuning on the second domain. We perform experiments across a variety of problem domains, both in simulation and on real robot. Our framework is able to align uncalibrated monocular video of a real robot arm to dynamic state-action trajectories of a simulated arm without paired data. Video demonstrations of our results are available at: https | LEARNING CROSS-DOMAIN CORRESPONDENCE FOR CONTROL WITH DYNAMICS CYCLE-CONSISTENCY |
d244527108 | We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward direction, we propose to include "retraced" transitions for representation/model learning, by enforcing the cycle-consistency constraint between the original and retraced states, hence improve upon the sample efficiency of learning. Moreover, learning via retracing explicitly propagates information about future transitions backward for inferring previous states, thus facilitates stronger representation learning for the downstream reinforcement learning tasks. We introduce Cycle-Consistency World Model (CCWM), a concrete model-based instantiation of learning via retracing. Additionally we propose a novel adaptive "truncation" mechanism for counteracting the negative impacts brought by "irreversible" transitions such that learning via retracing can be maximally effective. Through extensive empirical studies on visual-based continuous control benchmarks, we demonstrate that CCWM achieves state-of-the-art performance in terms of sample efficiency and asymptotic performance, whilst exhibiting behaviours that are indicative of stronger representation learning. * Please send any enquiry to changmin. yu.19@ucl.ac.uk and n.burgess@ucl.ac.uk | LEARNING STATE REPRESENTATIONS VIA RETRACING IN REINFORCEMENT LEARNING |
d231985599 | Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxiliary tasks do not take the characteristics of RL problems into consideration and are unsupervised. By leveraging returns, the most important feedback signals in RL, we propose a novel auxiliary task that forces the learnt representations to discriminate state-action pairs with different returns. Our auxiliary loss is theoretically justified to learn representations that capture the structure of a new form of state-action abstraction, under which state-action pairs with similar return distributions are aggregated together. In low data regime, our algorithm outperforms strong baselines on complex tasks in Atari games and DeepMind Control suite, and achieves even better performance when combined with existing auxiliary tasks. | RETURN-BASED CONTRASTIVE REPRESENTATION LEARNING FOR REINFORCEMENT LEARNING |
d254926918 | Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to major advances in forming object representations in an unsupervised fashion. Such systems learn representations invariant to augmentation operations over images, like cropping or flipping. In contrast, biological vision systems exploit the temporal structure of the visual experience during natural interactions with objects. This gives access to "augmentations" not commonly used in SSL, like watching the same object from multiple viewpoints or against different backgrounds. Here, we systematically investigate and compare the potential benefits of such time-based augmentations during natural interactions for learning object categories. Our results show that time-based augmentations achieve large performance gains over state-of-the-art image augmentations. Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation. Overall, we conclude that time-based augmentations during natural interactions with objects can substantially improve self-supervised learning, narrowing the gap between artificial and biological vision systems. * Equal contribution. | TIME TO AUGMENT SELF-SUPERVISED VISUAL REPRESENTATION LEARNING |
d229923865 | A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when hand-engineered reward functions are not available. Learned dynamics models are a promising approach for learning about the environment without rewards or task-directed data, but planning to reach goals with such a model requires a notion of functional similarity between observations and goal states. We present a self-supervised method for model-based visual goal reaching, which uses both a visual dynamics model as well as a dynamical distance function learned using model-free reinforcement learning. Our approach learns entirely using offline, unlabeled data, making it practical to scale to large and diverse datasets. In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot. In comparisons, we find that this approach substantially outperforms both model-free and model-based prior methods. Videos and visualizations are available here: https | MODEL-BASED VISUAL PLANNING WITH SELF-SUPERVISED FUNCTIONAL DISTANCES |
d260350986 | The recent progress in large language models (LLMs), especially the invention of chain-of-thought prompting, has made it possible to automatically answer questions by stepwise reasoning. However, when faced with more complicated problems that require non-linear thinking, even the strongest LLMs make mistakes. To address this, we explore whether LLMs are able to recognize errors in their own step-bystep reasoning, without resorting to external resources. To this end, we propose SelfCheck, a general-purpose zero-shot verification schema for recognizing such errors. We then use the results of these checks to improve question-answering performance by conducting weighted voting on multiple solutions to the question. We test SelfCheck on three datasets-GSM8K, MathQA, and MATH-and find that it successfully recognizes errors and, in turn, increases final answer accuracies.To this end, we introduce SelfCheck, a zero-shot step-by-step checker for self-identifying errors in LLM reasoning chains. SelfCheck uses the LLM to individually check the conditional correctness of each step in the chain based on the preceding steps, in a manner similar to a human going back to check their working. The results of these individual checks are then integrated to form an overall correctness estimation for the whole reasoning chain.Key to SelfCheck's success is a novel mechanism for performing the checking of individual steps. As we will show, the naive approach of directly asking the LLM to check a step is typically ineffective. Instead, we introduce a multi-stage approach that breaks the problem down into a series of simpler | SELFCHECK: USING LLMS TO ZERO-SHOT CHECK THEIR OWN STEP-BY-STEP REASONING |
d263605855 | Making LLaMA SEE and Draw with SEED Tokenizer | |
d202750126 | In natural language processing, it has been observed recently that generalization could be greatly improved by finetuning a large-scale language model pretrained on a large unlabeled corpus. Despite its recent success and wide adoption, finetuning a large pretrained language model on a downstream task is prone to degenerate performance when there are only a small number of training instances available. In this paper, we introduce a new regularization technique, to which we refer as "mixout", motivated by dropout. Mixout stochastically mixes the parameters of two models. We show that our mixout technique regularizes learning to minimize the deviation from one of the two models and that the strength of regularization adapts along the optimization trajectory. We empirically evaluate the proposed mixout and its variants on finetuning a pretrained language model on downstream tasks. More specifically, we demonstrate that the stability of finetuning and the average accuracy greatly increase when we use the proposed approach to regularize finetuning of BERT on downstream tasks in GLUE. | MIXOUT: EFFECTIVE REGULARIZATION TO FINETUNE LARGE-SCALE PRETRAINED LANGUAGE MODELS |
d3525078 | We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm-an iterative application of compressed sensing techniques for orthogonal polynomials-requires only uniform sampling of the hyperparameters and is thus easily parallelizable.Experiments for training deep nets on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases matching what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and even more so compared to Bayesian Optimization. We also outperform Random Search 5×.Additionally, our method comes with provable guarantees and yields the first quasipolynomial time algorithm for learning decision trees under the uniform distribution with polynomial sample complexity, the first improvement in over two decades. | Hyperparameter Optimization: A Spectral Approach |
d248084856 | Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNN architectures, and tools for understanding arbitrary GNN architectures are generally lacking. We provide an elegant way to easily obtain bounds on the separation power of GNNs in terms of the Weisfeiler-Leman (WL) tests, which have become the yardstick to measure the separation power of GNNs. The crux is to view GNNs as expressions in a procedural tensor language describing the computations in the layers of the GNNs. Then, by a simple analysis of the obtained expressions, in terms of the number of indexes and the nesting depth of summations, bounds on the separation power in terms of the WL-tests readily follow. We use tensor language to define Higher-Order Message-Passing Neural Networks (or k-MPNNs), a natural extension of MPNNs. Furthermore, the tensor language point of view allows for the derivation of universality results for classes of GNNs in a natural way. Our approach provides a toolbox with which GNN architecture designers can analyze the separation power of their GNNs, without needing to know the intricacies of the WL-tests. We also provide insights in what is needed to boost the separation power of GNNs.SPECIFYING GNNSMany GNNs use linear algebra computations on vectors, matrices or tensors, interleaved with the application of activation functions or MLPs. To understand the separation power of GNNs, we introduce a specification language, TL, for tensor language, that allows us to specify any algebraic computation in a procedural way by explicitly stating how each entry is to be computed. We gauge the separation power of GNNs by specifying them as TL expressions, and syntactically analyzing the components of such TL expressions. This technique gives rise to Higher-Order Message-Passing | EXPRESSIVENESS AND APPROXIMATION PROPERTIES OF GRAPH NEURAL NETWORKS |
d254198988 | Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple and yet effective regularization technique to boost the performance of deep models trained with SGD. In this work, we report a previously unobserved phenomenon in Mixup training: on a number of standard datasets, the performance of Mixup-trained models starts to decay after training for a large number of epochs, giving rise to a U-shaped generalization curve. This behavior is further aggravated when the size of original dataset is reduced. To help understand such a behavior of Mixup, we show theoretically that Mixup training may introduce undesired data-dependent label noises to the synthesized data. Via analyzing a least-square regression problem with a random feature model, we explain why noisy labels may cause the U-shaped curve to occur: Mixup improves generalization through fitting the clean patterns at the early training stage, but as training progresses, Mixup becomes over-fitting to the noise in the synthetic data. Extensive experiments are performed on a variety of benchmark datasets, validating this explanation. * Equal contribution.Published as a conference paper at ICLR 2023 epochs, the generalization performance of the network measured by its testing error may exhibit a U-shaped curve.Figure 1shows such a curve obtained from over-training ResNet18 with Mixup on CIFAR10. As can be seen fromFigure 1, after training with Mixup for a long time (200 epochs), both ERM and Mixup keep decreasing their training loss, but the testing error of the Mixup-trained ResNet18 gradually increases, while that of the ERM-trained ResNet18 continues to decrease.Motivated by this observation, we conduct a theoretical analysis, aiming to better understand the aforementioned behavior of Mixup training. We show theoretically that Mixup training may introduce undesired data-dependent label noises to the synthesized data. Then by analyzing the gradientdescent dynamics of training a random feature model for a least-square regression problem, we explain why noisy labels may cause the U-shaped curve to occur: under label noise, the early phase of training is primarily driven by the clean data pattern, which moves the model parameter closer to the correct solution. But as training progresses, the effect of label noise accumulates through iterations and gradually over-weighs that of the clean pattern and dominates the late training process. In this phase, the model parameter gradually moves away from the correct solution until it is sufficient apart and approaches a location depending on the noise realization. | OVER-TRAINING WITH MIXUP MAY HURT GENERALIZATION |
d44981793 | Recent breakthroughs in computer vision make use of large deep neural networks, utilizing the substantial speedup offered by GPUs. For applications running on limited hardware however, high precision real-time processing can still be a challenge. One approach to solve this problem is learning networks with binary or ternary weights, thus removing the need to calculate multiplications and significantly reduce memory size and access. In this work we introduce LR-nets (Local reparameterization networks), a new method for training neural networks with discrete weights using stochastic parameters. We show how a simple modification to the local reparameterization trick, previously used to train Gaussian distributed weights, allows us to train discrete weights. We tested our method on MNIST, CIFAR-10 and ImageNet, achieving state-of-the-art results compared to previous binary and ternary models. | Learning Discrete Weights Using the Local Reparameterization Trick |
d253581364 | Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, most of the voice synthesis models still require a large number of audio data paired with annotated labels (e.g., text transcription and music score) for training. To this end, we propose a unified framework of synthesizing and manipulating voice signals from analysis features, dubbed NANSY++. The backbone network of NANSY++ is trained in a self-supervised manner that does not require any annotations paired with audio. After training the backbone network, we efficiently tackle four voice applications -i.e. voice conversion, text-to-speech, singing voice synthesis, and voice designing -by partially modeling the analysis features required for each task. Extensive experiments show that the proposed framework offers competitive advantages such as controllability, data efficiency, and fast training convergence, while providing high quality synthesis. Audio samples: tinyurl.com/8tnsy3uc. | NANSY++: UNIFIED VOICE SYNTHESIS WITH NEURAL ANALYSIS AND SYNTHESIS |
d251018503 | Test-time adaptation (TTA) aims to adapt a trained classifier using online unlabeled test data only, without any information related to the training procedure. Most existing TTA methods adapt the trained classifier using the classifier's prediction on the test data as pseudo-label. However, under test-time domain shift, accuracy of the pseudo labels cannot be guaranteed, and thus the TTA methods often encounter performance degradation at the adapted classifier. To overcome this limitation, we propose a novel test-time adaptation method, called Test-time Adaptation via Self-Training with nearest neighbor information (TAST), which is composed of the following procedures: (1) adds trainable adaptation modules on top of the trained feature extractor; (2) newly defines a pseudo-label distribution for the test data by using the nearest neighbor information; (3) trains these modules only a few times during test time to match the nearest neighbor-based pseudo label distribution and a prototype-based class distribution for the test data; and (4) predicts the label of test data using the average predicted class distribution from these modules. The pseudo-label generation is based on the basic intuition that a test data and its nearest neighbor in the embedding space are likely to share the same label under the domain shift. By utilizing multiple randomly initialized adaptation modules, TAST extracts useful information for the classification of the test data under the domain shift, using the nearest neighbor information. TAST showed better performance than the state-of-the-art TTA methods on two standard benchmark tasks, domain generalization, namely VLCS, PACS, OfficeHome, and TerraIncognita, and image corruption, particularly CIFAR-10/100C. Our code is available at https://github.com/mingukjang/TAST. | TEST-TIME ADAPTATION VIA SELF-TRAINING WITH NEAREST NEIGHBOR INFORMATION |
d250626929 | Recently the SP (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. We take a step further and develop a method for solving the interpolation equations that uses the local second-order approximation of the model. Our resulting method SP2 uses Hessian-vector products to speed-up the convergence of SP. Furthermore, and rather uniquely among second-order methods, the design of SP2 in no way relies on positive definite Hessian matrices or convexity of the objective function. We show SP2 is very competitive on matrix completion, non-convex test problems and logistic regression. We also provide a convergence theory on sums-of-quadratics. | SP2 : A Second Order Stochastic Polyak Method |
d252683534 | Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this paper, we study the evaluation of counterfactual statements through neural models. Specifically, we tackle two causal problems required to make such evaluations, i.e., counterfactual identification and estimation from an arbitrary combination of observational and experimental data. First, we show that neural causal models (NCMs) are expressive enough and encode the structural constraints necessary for performing counterfactual reasoning. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions. We show that this algorithm is sound and complete for deciding counterfactual identification in general settings. Third, considering the practical implications of these results, we introduce a new strategy for modeling NCMs using generative adversarial networks. Simulations corroborate with the proposed methodology.Each SCM M assigns values to each counterfactual distribution as follows:Each Y i corresponds to a set of variables in a world where the original mechanisms f X are replaced with constants x i for each X ∈ X i ; this is also known as the mutilation procedure. This procedure corresponds to interventions, and we use subscripts to denote the intervening variables (e.g. Y x ) or subscripts with brackets when the variables are indexed (e.g.Y 1[x1]). For instance, P (y x , y x ) is the probability of the joint counterfactual event Y = y had X been x and Y = y had X been x . 6 Witty et al. (2021) shows a related approach taking the Bayesian route; further details, see Appendix C.3 SCM M 2 is said to be P (Li) -consistent (for short, L i -consistent) with SCM M 1 if L i (M 1 ) = L i (M 2 ). We will use Z to denote a set of quantities from Layer 2 (i.e. Z = {P (V z k )} k=1 ), and we use Z(M) to denote those same quantities induced by SCM M (i.e. Z(M) = {P M (V z k )} k=1 ).We use neural causal models (NCMs) as a substitute (proxy) model for the true SCM, as follows: Definition 2 (G-Constrained Neural Causal Model (G-NCM) (Xia et al., 2021, Def. 7)). Given a causal diagram G, a G-constrained Neural Causal Model (for short, G-NCM) M (θ) over variables V with parameters θ = {θ Vi : V i ∈ V} is an SCM U, V, F, P ( U) such that U = { U C : C ∈ C(G)}, where C(G) is the set of all maximal cliques over bidirected edges of G, and D U = [0, 1] for all U ∈ U; F = {f Vi : V i ∈ V}, where eachf Vi is a feedforward neural network parameterized by θ Vi ∈ θ mapping values of U Vi ∪ Pa Vi to values of V i for U Vi = { U C : U C ∈ U s.t. V i ∈ C} and Pa Vi = P a G (V i ); P ( U) is defined s.t. U ∼ Unif(0, 1) for each U ∈ U. | NEURAL CAUSAL MODELS FOR COUNTERFACTUAL IDENTIFICATION AND ESTIMATION |
d231847056 | Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases e.g. message passing methods are run differently from node embedding ones. Despite their apparent differences, all these methods utilize the graph structure, and therefore, their learning can be approximated with stochastic graph traversals. We propose Graph Traversal via Tensor Functionals (GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs. GTTF is founded upon a data structure (stored as a sparse tensor) and a stochastic graph traversal algorithm (described using tensor operations). The algorithm is a functional that accept two functions, and can be specialized to obtain a variety of GRL models and objectives, simply by changing those two functions. We show for a wide class of methods, our algorithm learns in an unbiased fashion and, in expectation, approximates the learning as if the specialized implementations were run directly. With these capabilities, we scale otherwise non-scalable methods to set state-of-the-art on large graph datasets while being more efficient than existing GRL libraries -with only a handful of lines of code for each method specialization. GTTF and its various GRL implementations are on: | GRAPH TRAVERSAL WITH TENSOR FUNCTIONALS: A META-ALGORITHM FOR SCALABLE LEARNING |
d260378993 | Sparse mixture of expert architectures (MoEs) scale model capacity without large increases in training or inference costs. Despite their success, MoEs suffer from a number of issues: training instability, token dropping, inability to scale the number of experts, or ineffective finetuning. In this work, we propose Soft MoE, a fully-differentiable sparse Transformer that addresses these challenges, while maintaining the benefits of MoEs. Soft MoE performs an implicit soft assignment by passing different weighted combinations of all input tokens to each expert. As in other MoE works, experts in Soft MoE only process a subset of the (combined) tokens, enabling larger model capacity at lower inference cost. In the context of visual recognition, Soft MoE greatly outperforms standard Transformers (ViTs) and popular MoE variants (Tokens Choice and Experts Choice). For example, Soft MoE-Base/16 requires 10.5× lower inference cost (5.7× lower wall-clock time) than ViT-Huge/14 while matching its performance after similar training. Soft MoE also scales well: Soft MoE Huge/14 with 128 experts in 16 MoE layers has over 40× more parameters than ViT Huge/14, while inference time cost grows by only 2%, and it performs substantially better. * Equal contribution. The order was decided by a coin toss. 1 arXiv:2308.00951v1 [cs.LG] 2 Aug 2023 1 def soft_m oe_lay er (X , Phi , experts ) : 2 # Compute the dispatch and combine weights .3 logits = jnp . einsum ( 'md , dnp -> mnp ' , X , Phi ) 4 D = jax . nn . softmax ( logits , axis =(0 ,) ) 5 C = jax . nn . softmax ( logits , axis =(1 , 2) ) 6 # The input slots are a weighted average of all the input tokens , 7 # given by the dispatch weights .8 Xs = jnp . einsum ( 'md , mnp -> npd ' , X , D ) 9 # Apply the corresponding expert function to each input slot .10 Ys = jnp . stack ([ 11 f_i ( Xs [i , : , :]) for i , f_i in enumerate ( experts ) ] , 12 axis =0) 13 # The output tokens are a weighted average of all the output slots , 14 # given by the combine weights . 15 Y = jnp . einsum ( 'npd , mnp -> md ' , Ys , C ) 16 return Y Algorithm 1: Simple JAX (Bradbury et al., 2018) implementation of a Soft MoE layer. Full code is available at https://github.com/google-research/vmoe. | From Sparse to Soft Mixtures of Experts |
d235743242 | In observational studies, balancing covariates in different treatment groups is essential to estimate treatment effects. One of the most commonly used methods for such purposes is weighting. The performance of this class of methods usually depends on strong regularity conditions for the underlying model, which might not hold in practice.In this paper, we investigate weighting methods from a functional estimation perspective and argue that the weights needed for covariate balancing could differ from those needed for treatment effects estimation under low regularity conditions. Motivated by this observation, we introduce a new framework of weighting that directly targets the treatment effects estimation. Unlike existing methods, the resulting estimator for a treatment effect under this new framework is a simple kernel-based U -statistic after applying a data-driven transformation to the observed covariates. We characterize the theoretical properties of the new estimators of treatment effects under a nonparametric setting and show that they are able to work robustly under low regularity conditions.The new framework is also applied to several numerical examples to demonstrate its practical merits. | Treatment Effects Estimation by Uniform Transformer |
d52901536 | While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of deterministic weights, while applying certain techniques such as pruning and quantization, in order that the empirical weight distribution becomes amenable to Shannon-style coding schemes. However, as shown in this paper, relaxing weight determinism and using a full variational distribution over weights allows for more efficient coding schemes and consequently higher compression rates. In particular, following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution. By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set. The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family. Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance. | MINIMAL RANDOM CODE LEARNING: GETTING BITS BACK FROM COMPRESSED MODEL PARAMETERS |
d247450787 | We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many safety-critical domains. However, because they can use nondifferentiable operations, it is hard to learn their parameters using existing gradient-based approaches to safe learning. Our approach to this problem, Differentiable Symbolic Execution (DSE), samples control flow paths in a program, symbolically constructs worst-case "safety losses" along these paths, and backpropagates the gradients of these losses through program operations using a generalization of the REINFORCE estimator. We evaluate the method on a mix of synthetic tasks and real-world benchmarks. Our experiments show that DSE significantly outperforms the state-of-the-art DIFFAI method on these tasks. 1 | SAFE NEUROSYMBOLIC LEARNING WITH DIFFERENTIABLE SYMBOLIC EXECUTION |
d252693138 | Traditional recurrent neural networks (RNNs) have a fixed, finite number of memory cells. In theory (assuming bounded range and precision), this limits their formal language recognition power to regular languages, and in practice, RNNs have been shown to be unable to learn many context-free languages (CFLs). In order to expand the class of languages RNNs recognize, prior work has augmented RNNs with a nondeterministic stack data structure, putting them on par with pushdown automata and increasing their language recognition power to CFLs. Nondeterminism is needed for recognizing all CFLs (not just deterministic CFLs), but in this paper, we show that nondeterminism and the neural controller interact to produce two more unexpected abilities. First, the nondeterministic stack RNN can recognize not only CFLs, but also many non-context-free languages. Second, it can recognize languages with much larger alphabet sizes than one might expect given the size of its stack alphabet. Finally, to increase the information capacity in the stack and allow it to solve more complicated tasks with large alphabet sizes, we propose a new version of the nondeterministic stack that simulates stacks of vectors rather than discrete symbols. We demonstrate perplexity improvements with this new model on the Penn Treebank language modeling benchmark. | THE SURPRISING COMPUTATIONAL POWER OF NONDETERMINISTIC STACK RNNS |
d229153786 | In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the multiple timescales of contextual information are functionally organized. Therefore, we applied tools developed in neuroscience to map the "processing timescales" of individual units within a word-level LSTM language model. This timescalemapping method assigned long timescales to units previously found to track longrange syntactic dependencies, and revealed a new cluster of previously unreported long-timescale units. Next, we explored the functional role of units by examining the relationship between their processing timescales and network connectivity. We identified two classes of long-timescale units: "Controller" units composed a densely interconnected subnetwork and strongly projected to the forget and input gates of the rest of the network, while "Integrator" units showed the longest timescales in the network, and expressed projection profiles closer to the mean projection profile. Ablating integrator and controller units affected model performance at different position of a sentence, suggesting distinctive functions of these two sets of units. Finally, we tested the generalization of these results to a character-level LSTM model. In summary, we demonstrated a model-free technique for mapping the timescale organization in neural network models, and we applied this method to reveal the timescale and functional organization of LSTM language models. 1 | MAPPING THE TIMESCALE ORGANIZATION OF NEURAL LANGUAGE MODELS |
d248118520 | Neural processes (NPs) aim to stochastically complete unseen data points based on a given context dataset. NPs essentially leverage a given dataset as a context representation to derive a suitable identifier for a novel task. To improve the prediction accuracy, many variants of NPs have investigated context embedding approaches that generally design novel network architectures and aggregation functions satisfying permutation invariant. In this work, we propose a stochastic attention mechanism for NPs to capture appropriate context information. From the perspective of information theory, we demonstrate that the proposed method encourages context embedding to be differentiated from a target dataset, allowing NPs to consider features in a target dataset and context embedding independently. We observe that the proposed method can appropriately capture context embedding even under noisy data sets and restricted task distributions, where typical NPs suffer from a lack of context embeddings. We empirically show that our approach substantially outperforms conventional NPs in various domains through 1D regression, predator-prey model, and image completion. Moreover, the proposed method is also validated by MovieLens-10k dataset, a real-world problem. -learning surrogate models for sequential decision making. arXiv preprint arXiv: | NEURAL PROCESSES WITH STOCHASTIC ATTENTION: PAYING MORE ATTENTION TO THE CONTEXT DATASET |
d247696299 | Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification.However, these models assume that the dynamics are fixed and unchanging, which is rarely the case in realworld scenarios.Many control applications often exhibit tasks with similar but not identical dynamics which can be modeled as a latent variable.We introduce the Hidden Parameter Recurrent State Space Models (HiP-RSSMs), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors.We present a simple and effective way of learning and performing inference over this Gaussian graphical model that avoids approximations like variational inference.We show that HiP-RSSMs outperforms RSSMs and competing multi-task models on several challenging robotic benchmarks both on real-world systems and simulations.We found that existing literature on recurrent models fails to model the dynamics accurately in these situations.Thus, we introduce hidden parameter state-space models (HiP-SSM), which allow capturing the variation in the dynamics of different instances through a set of hidden task parameters. | HIDDEN PARAMETER RECURRENT STATE SPACE MODELS FOR CHANGING DYNAMICS SCENARIOS |
d225094654 | Despite the widespread application of recurrent neural networks (RNNs), a unified understanding of how RNNs solve particular tasks remains elusive. In particular, it is unclear what dynamical patterns arise in trained RNNs, and how those patterns depend on the training dataset or task. This work addresses these questions in the context of text classification, building on earlier work studying the dynamics of binary sentiment-classification networks (Maheswaranathan et al., 2019). We study text-classification tasks beyond the binary case, exploring the dynamics of RNNs trained on both natural and synthetic datasets. These dynamics, which we find to be both interpretable and low-dimensional, share a common mechanism across architectures and datasets: specifically, these text-classification networks use low-dimensional attractor manifolds to accumulate evidence for each class as they process the text. The dimensionality and geometry of the attractor manifold are determined by the structure of the training dataset, with the dimensionality reflecting the number of scalar quantities the network remembers in order to classify. In categorical classification, for example, we show that this dimensionality is one less than the number of classes. Correlations in the dataset, such as those induced by ordering, can further reduce the dimensionality of the attractor manifold; we show how to predict this reduction using simple word-count statistics computed on the training dataset. To the degree that integration of evidence towards a decision is a common computational primitive, this work continues to lay the foundation for using dynamical systems techniques to study the inner workings of RNNs. * Work started while an intern at Google. † Equal contribution.arXiv:2010.15114v2 [cs.LG] 3 Jun 2022Recent work has shown that modern RNN architectures trained on binary sentiment classification learn low-dimensional, interpretable dynamical systems(Maheswaranathan et al., 2019). These RNNs were found to implement an integration-like mechanism, moving their hidden states along a line of stable fixed points to keep track of accumulated positive and negative tokens. Later, Maheswaranathan & Sussillo (2020) showed that contextual processing mechanisms in these networkse.g. for handling phrases like not good-build on top of the line-integration mechanism, employing an additional subspace which the network enters upon encountering a modifier word. The understanding achieved in those works suggests the potential of the dynamical systems perspective, but it remained to be seen whether this perspective could shed light on RNNs in more complicated settings. | THE GEOMETRY OF INTEGRATION IN TEXT CLASSIFICATION RNNS |
d36549760 | Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retraining the model. By post-hoc learning latent constraints, value functions that identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradient-based optimization or amortized actor functions. Combining attribute constraints with a universal "realism" constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder. Further, using gradient-based optimization, we demonstrate identity-preserving transformations that make the minimal adjustment in latent space to modify the attributes of an image. Finally, with discrete sequences of musical notes, we demonstrate zero-shot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function. Code with dedicated cloud instance has been made publicly available (https://goo.gl/STGMGx). | LATENT CONSTRAINTS: LEARNING TO GENERATE CONDITIONALLY FROM UNCONDITIONAL GENERATIVE MODELS |
d263830051 | Optimal Transport is a useful metric to compare probability distributions and to compute a pairing given a ground cost.Its entropic regularization variant (eOT) is crucial to have fast algorithms and reflect fuzzy/noisy matchings.This work focuses on Inverse Optimal Transport (iOT), the problem of inferring the ground cost from samples drawn from a coupling that solves an eOT problem.It is a relevant problem that can be used to infer unobserved/missing links, and to obtain meaningful information about the structure of the ground cost yielding the pairing.On one side, iOT benefits from convexity, but on the other side, being ill-posed, it requires regularization to handle the sampling noise.This work presents an in-depth theoretical study of the ℓ 1 regularization to model for instance Euclidean costs with sparse interactions between features.Specifically, we derive a sufficient condition for the robust recovery of the sparsity of the ground cost that can be seen as a far reaching generalization of the Lasso's celebrated "Irrepresentability Condition".To provide additional insight into this condition, we work out in detail the Gaussian case.We show that as the entropic penalty varies, the iOT problem interpolates between a graphical Lasso and a classical Lasso, thereby establishing a connection between iOT and graph estimation, an important problem in ML. | Sparsistency for Inverse Optimal Transport |
d239015813 | Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks. Yet, they are less popular for practical deployments in the industry owing to their scalability challenges incurred by data dependency. Namely, GNN inference depends on neighbor nodes multiple hops away from the target, and fetching them burdens latency-constrained applications. Existing inference acceleration methods like pruning and quantization can speed up GNNs by reducing Multiplication-and-ACcumulation (MAC) operations, but the improvements are limited given the data dependency is not resolved. Conversely, multi-layer perceptrons (MLPs) have no graph dependency and infer much faster than GNNs, even though they are less accurate than GNNs for node classification in general. Motivated by these complementary strengths and weaknesses, we bring GNNs and MLPs together via knowledge distillation (KD). Our work shows that the performance of MLPs can be improved by large margins with GNN KD. We call the distilled MLPs Graph-less Neural Networks (GLNNs) as they have no inference graph dependency. We show that GLNNs with competitive accuracy infer faster than GNNs by 146×-273× and faster than other acceleration methods by 14×-27×. Under a production setting involving both transductive and inductive predictions across 7 datasets, GLNN accuracies improve over stand-alone MLPs by 12.36% on average and match GNNs on 6/7 datasets. Comprehensive analysis shows when and why GLNNs can achieve competitive accuracies to GNNs and suggests GLNN as a handy choice for latency-constrained applications.Published as a conference paper at ICLR 2022 However, their improvements are limited given the graph dependency is not resolved. Unlike GNNs, MLPs have no dependency on graph data and are easier to deploy than GNNs. They also enjoy the auxiliary benefit of sidestepping the cold-start problem that often happens during the online prediction of relational data , meaning MLPs can infer reasonably even when neighbor information of a new encountered node is not immediately available. On the other hand, this lack of graph dependency typically hurts for relational learning tasks, limiting MLP performance on GML tasks compared to GNNs. We thus ask: can we bridge the two worlds, enjoying the low-latency, dependency-free nature of MLPs and the graph context-awareness of GNNs at the same time?Present work. Our key finding is that it is possible to distill knowledge from GNNs to MLPs without losing significant performance, but reducing the inference time drastically for node classification. The knowledge distillation (KD) can be done offline, coupled with model training. In other words, we can shift considerable work from the latency-constrained inference step, where time reduction in milliseconds makes a huge difference, to the less time-sensitive training step, where time cost in hours or days is often tolerable. We call our approach Graph-less Neural Network (GLNN). Specifically, GLNN is a modeling paradigm involving KD from a GNN teacher to a student MLP; the resulting GLNN is an MLP optimized through KD, so it enjoys the benefits of graph contextawareness in training but has no graph dependency in inference. Regarding speed, GLNNs have superior efficiency and are 146×-273× faster than GNNs and 14×-27× faster than other inference acceleration methods. Regarding performance, under a production setting involving both transductive and inductive predictions on 7 datasets, GLNN accuracies improve over MLPs by 12.36% on average and match GNNs on 6/7 datasets. We comprehensively study when and why GLNNs can achieve competitive results as GNNs. Our analysis suggests the critical factors for such great performance are large MLP sizes and high mutual information between node features and labels. Our observations align with recent results in vision and language, which posit that large enough (or slightly modified) MLPs can achieve similar results as CNNs and Transformers(Liu et al., 2021;Tolstikhin et al., 2021;Melas-Kyriazi, 2021;Touvron et al., 2021;Ding et al., 2021). Our core contributions are as follows:• We propose GLNN, which eliminates neighbor-fetching latency in GNN inference via KD to MLP. • We show GLNNs has competitive performance as GNNs, while enjoying 146×-273× faster inference than vanilla GNNs and 14×-27× faster inference than other inference acceleration methods. • We study GLNN properties comprehensively by investigating their performance under different settings, how they work as regularizers, their inductive bias, expressiveness, and limitations. | GRAPH-LESS NEURAL NETWORKS: TEACHING OLD MLPS NEW TRICKS VIA DISTILLATION |
d259088843 | Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretation, whereas classical robotics addresses this with scene reconstruction resulting in some form of map, usually estimated with geometry and sensor models and/or learning. In this work we propose to learn an actionable representation of the scene independently of the targeted downstream task and without explicitly optimizing reconstruction. The learned representation is optimized by a blind auxiliary agent trained to navigate with it on multiple short sub episodes branching out from a waypoint and, most importantly, without any direct visual observation. We argue and show that the blindness property is important and forces the (trained) latent representation to be the only means for planning. With probing experiments we show that the learned representation optimizes navigability and not reconstruction. On downstream tasks we show that it is robust to changes in distribution, in particular the sim2real gap, which we evaluate with a real physical robot in a real office building, significantly improving performance. | LEARNING WITH A MOLE: TRANSFERABLE LATENT SPATIAL REPRESENTATIONS FOR NAVIGATION WITHOUT RECONSTRUCTION |
d263609006 | An essential and challenging problem in causal inference is causal effect estimation from observational data.The problem becomes more difficult with the presence of unobserved confounding variables.The front-door adjustment is a practical approach for dealing with unobserved confounding variables.However, the restriction for the standard front-door adjustment is difficult to satisfy in practice.In this paper, we relax some of the restrictions by proposing the concept of conditional front-door (CFD) adjustment and develop the theorem that guarantees the causal effect identifiability of CFD adjustment.Furthermore, as it is often impossible for a CFD variable to be given in practice, it is desirable to learn it from data.By leveraging the ability of deep generative models, we propose CFDiVAE to learn the representation of the CFD adjustment variable directly from data with the identifiable Variational AutoEncoder and formally prove the model identifiability.Extensive experiments on synthetic datasets validate the effectiveness of CFDiVAE and its superiority over existing methods.The experiments also show that the performance of CFDiVAE is less sensitive to the causal strength of unobserved confounding variables.We further apply CFDiVAE to a real-world dataset to demonstrate its potential application. | CAUSAL INFERENCE WITH CONDITIONAL FRONT-DOOR ADJUSTMENT AND IDENTIFIABLE VARIATIONAL AUTOENCODER |
d263830401 | We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory.We perform extensive evaluations on two datasets: (i) the existing 'Strogatz' dataset featuring twodimensional systems; (ii) ODEBench, a collection of one-to four-dimensional systems that we carefully curated from the literature to provide a more holistic benchmark.ODEFormer consistently outperforms existing methods while displaying substantially improved robustness to noisy and irregularly sampled observations, as well as faster inference.We release our code, model and benchmark dataset publicly. | ODEFormer: Symbolic Regression of Dynamical Systems with Transformers |
d227746856 | Consider a prediction setting where a few inputs (e.g., satellite images) are expensively annotated with the prediction targets (e.g., crop types), and many inputs are cheaply annotated with auxiliary information (e.g., climate information). How should we best leverage this auxiliary information for the prediction task? Empirically across three image and time-series datasets, and theoretically in a multi-task linear regression setting, we show that (i) using auxiliary information as input features improves in-distribution error but can hurt out-of-distribution (OOD) error; while (ii) using auxiliary information as outputs of auxiliary tasks to pre-train a model improves OOD error. To get the best of both worlds, we introduce In-N-Out, which first trains a model with auxiliary inputs and uses it to pseudolabel all the in-distribution inputs, then pre-trains a model on OOD auxiliary outputs and finetunes this model with the pseudolabels (self-training). We show both theoretically and empirically that In-N-Out outperforms auxiliary inputs or outputs alone on both in-distribution and OOD error.In remote sensing, satellite imagery is paired with aux-inputs to predict the desired output[49,54]. Aux-inputs can provide more features to improve in-distribution performance, and one may hope that this can also improve OOD performance. Indeed, previous results on standard datasets [38, 53, 41] show that improved in-distribution accuracy correlates with improves OOD accuracy. However, in this paper we find that while aux-inputs often help in-distribution error, they can hurt OOD error.Conversely, aux-output methods such as pre-training, transfer learning, and multi-task learning may improve OOD performance by changing the inductive bias of the model through auxiliary supervi- * Equal contribution. | In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness |
d56895592 | Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data. To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD. Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function. The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets. Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions. The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization. 1dense, linear 4 × 4, stride 2 deconv, 256, BN, ReLU 4 × 4, stride 2 deconv, 128, BN, ReLU 4 × 4, stride 2 deconv, 64, BN, ReLU 3 × 3, stride 1 conv, 3, Tanh (b) Discriminator RGB image x ∈ [−1, 1] H×W ×3 3 × 3, stride 1 conv, 64, LReLU 4 × 4, stride 2 conv, 128, LReLU 3 × 3, stride 1 conv, 128, LReLU 4 × 4, stride 2 conv, 256, LReLU 3 × 3, stride 1 conv, 256, LReLU 4 × 4, stride 2 conv, 512, LReLU 3 × 3, stride 1 conv, 512, LReLU h × w × 512 → s, dense, linearTable S2: DCGAN models for image generation on CelebA and LSUN-bedroom datasets. For non-saturating loss and hinge loss, s = 1; for MMD-rand, MMD-rbf, MMD-rq, s = 16. (a) Generator z ∈ R 128 ∼ N (0, I) 128 → 4 × 4 × 1024, dense, linear 4 × 4, stride 2 deconv, 512, BN, ReLU 4 × 4, stride 2 deconv, 256, BN, ReLU 4 × 4, stride 2 deconv, 128, BN, ReLU 4 × 4, stride 2 deconv, 64, BN, ReLU 3 × 3, stride 1 conv, 3, Tanh (b) Discriminator RGB image x ∈ [−1, 1] 64×64×3 3 × 3, stride 1 conv, 64, LReLU 4 × 4, stride 2 conv, 128, LReLU 3 × 3, stride 1 conv, 128, LReLU 4 × 4, stride 2 conv, 256, LReLU 3 × 3, stride 1 conv, 256, LReLU 4 × 4, stride 2 conv, 512, LReLU 3 × 3, stride 1 conv, 512, LReLU 4 × 4, stride 2 conv, 1024, LReLU 3 × 3, stride 1 conv, 1024, LReLU 4 × 4 × 512 → s, dense, linear | IMPROVING MMD-GAN TRAINING WITH REPULSIVE LOSS FUNCTION |
d67856640 | Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss However, we reveal that this training recipe shared by almost all existing methods causes one critical side effect: lack of diversity in output samples. In order to accomplish both training stability and multimodal output generation, we propose novel training schemes with a new set of losses named moment reconstruction losses that simply replace the reconstruction loss. We show that our approach is applicable to any conditional generation tasks by performing thorough experiments on image-to-image translation, super-resolution and image inpainting using Cityscapes and CelebA dataset. Quantitative evaluations also confirm that our methods achieve a great diversity in outputs while retaining or even improving the visual fidelity of generated samples.In summary, our major contributions are three-fold. First, we show that there is a significant mismatch between the GAN loss and the reconstruction loss, thereby the model cannot achieve the optimality w.r.t. both losses. Second, we propose two novel loss functions that enable the model to accomplish both training stability and multimodal output generation. Our methods simply replace the reconstruction loss, and thus are applicable to any conditional generation tasks. Finally, we show the effectiveness and generality of our methods through extensive experiments on three generation tasks, including image-to-image translation, super-resolution and image inpainting, where our methods outperform recent strong baselines in terms of realism and diversity. | HARMONIZING MAXIMUM LIKELIHOOD WITH GANS FOR MULTIMODAL CONDITIONAL GENERATION |
d252545361 | Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are only trained on a few hundreds or thousands of images as opposed to the millions of examples deep networks are trained on in other domains. In this work, we study whether major performance gains are expected from scaling up the training set size. We consider image denoising, accelerated magnetic resonance imaging, and super-resolution and empirically determine the reconstruction quality as a function of training set size, while simultaneously scaling the network size. For all three tasks we find that an initially steep power-law scaling slows significantly already at moderate training set sizes. Interpolating those scaling laws suggests that even training on millions of images would not significantly improve performance. To understand the expected behavior, we analytically characterize the performance of a linear estimator learned with early stopped gradient descent. The result formalizes the intuition that once the error induced by learning the signal model is small relative to the error floor, more training examples do not improve performance. | Scaling Laws For Deep Learning Based Image Reconstruction |
d221095529 | We develop a methodology for assessing the robustness of models to subpopulation shift-specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. This enables us to synthesize realistic distribution shifts whose sources can be precisely controlled and characterized, within existing large-scale datasets. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity. We then validate that the corresponding shifts are tractable by obtaining human baselines for them. Finally, we utilize these benchmarks to measure the sensitivity of standard model architectures as well as the effectiveness of off-the-shelf train-time robustness interventions. 1 Dalmatians as "dogs" even when their training data for "dogs" comprises only Poodles and Terriers. We show how one can simulate such shifts, fairly naturally, within existing datasets, hence eliminating the need for (and the potential biases introduced by) crafting synthetic transformations or collecting additional data.BREEDS benchmarks.The crux of our approach is to leverage existing dataset labels and use them to identify superclasses-i.e., groups of semantically similar classes. This allows us to construct classification tasks over such superclasses, and repurpose the original dataset classes to be the subpopulations of interest. This, in turn, enables us to induce a subpopulation shift by directly making the subpopulations present in the training and test distributions disjoint. By applying this methodology to the ImageNet dataset [Den+09], we create a suite of subpopulation shift benchmarks of varying difficulty. This involves modifying the existing ImageNet class hierarchy-WordNet [Mil95]-to ensure that superclasses comprise visually coherent subpopulations. We then conduct human studies to validate that the resulting BREEDS benchmarks indeed capture meaningful subpopulation shifts.Model robustness to subpopulation shift. In order to demonstrate the utility of our benchmarks, we employ them to evaluate the robustness of standard models to subpopulation shift. In general, we find that model performance drops significantly on the shifted distribution-even when this shift does not significantly affect humans. Still, models that are more accurate on the original distribution tend to also be more robust to these subpopulation shifts. Moreover, adapting models to the shifted domain, by retraining their last layer on data from this domain, only partially recovers the original model performance.SourceTarget garment trench coat,Table 3: Superclasses used for the ENTITY-13 task, along with the corresponding subpopulations that comprise the source and target domains. | BREEDS: Benchmarks for Subpopulation Shift |
d52902256 | As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should be predicted from the training data and network parameters. In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap. Our measure is based on the concept of margin distribution, which are the distances of training points to the decision boundary. We find that it is necessary to use margin distributions at multiple layers of a deep network. On the CIFAR-10 and the CIFAR-100 datasets, our proposed measure correlates very strongly with the generalization gap. In addition, we find the following other factors to be of importance: normalizing margin values for scale independence, using characterizations of margin distribution rather than just the margin (closest distance to decision boundary), and working in log space instead of linear space (effectively using a product of margins rather than a sum). Our measure can be easily applied to feedforward deep networks with any architecture and may point towards new training loss functions that could enable better generalization. * Work done as a Google AI Resident. | Predicting the Generalization Gap in Deep Networks with Margin Distributions |
d3524955 | Interpreting deep neural networks can enable new applications for predictive modeling where both accuracy and interpretability are required. In this paper, we examine the weights of a deep neural network to interpret the statistical interactions it captures. Our key observation is that any input features that interact with each other must follow strongly weighted paths to a common hidden unit before the final output. We propose a novel framework, which we call Neural Interaction Detector (NID), that identifies meaningful interactions of arbitrary-order without an exhaustive search on an exponential solution space of interaction candidates. Empirical evaluation on both synthetic and real-world data showed the effectiveness of NID, which detects interactions more accurately and efficiently than does the state-of-the-art. | Detecting Statistical Interactions from Neural Network Weights |
d246634950 | Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this paper, we focus on sparse training and highlight a perhaps counter-intuitive finding, that random pruning at initialization can be quite powerful for the sparse training of modern neural networks. Without any delicate pruning criteria or carefully pursued sparsity structures, we empirically demonstrate that sparsely training a randomly pruned network from scratch can match the performance of its dense equivalent. There are two key factors that contribute to this revival: (i) the network sizes matter: as the original dense networks grow wider and deeper, the performance of training a randomly pruned sparse network will quickly grow to matching that of its dense equivalent, even at high sparsity ratios; (ii) appropriate layer-wise sparsity ratios can be pre-chosen for sparse training, which shows to be another important performance booster. Simple as it looks, a randomly pruned subnetwork of Wide ResNet-50 can be sparsely trained to outperforming a dense Wide ResNet-50, on ImageNet. We also observed such randomly pruned networks outperform dense counterparts in other favorable aspects, such as out-of-distribution detection, uncertainty estimation, and adversarial robustness. Overall, our results strongly suggest there is larger-thanexpected room for sparse training at scale, and the benefits of sparsity might be more universal beyond carefully designed pruning. Our source code can be found at https://github.com/VITA-Group/Random_Pruning. | THE UNREASONABLE EFFECTIVENESS OF RANDOM PRUNING: RETURN OF THE MOST NAIVE BASELINE FOR SPARSE TRAINING |
d246680129 | Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous optimization approaches can be "reinvented" in our framework, and further propose new probabilistic black-box optimization algorithms. Extensive experiments on sequence design application illustrate the benefits of the proposed methodology. approximate bayesian computation. Biometrika, 96:983-990, 2009. Volodimir Begy and Erich Schikuta. Error-guided likelihood-free mcmc. , et al. Prottrans: towards cracking the language of life's code through self-supervised deep learning and high performance computing. arXiv preprint arXiv:2007.06225, 2020. Paul Fearnhead and Dennis Prangle. Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation (with discussion). 2012. | UNIFYING LIKELIHOOD-FREE INFERENCE WITH BLACK-BOX OPTIMIZATION AND BEYOND |
d263834884 | As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these "LLM evaluators", particularly in using them to assess instruction following, a metric that gauges how closely generated text adheres to the given instruction. We introduce a challenging meta-evaluation benchmark, LLM-BAR, designed to test the ability of an LLM evaluator in discerning instructionfollowing outputs. The authors manually curated 419 pairs of outputs, one adhering to instructions while the other diverging, yet may possess deceptive qualities that mislead an LLM evaluator, e.g., a more engaging tone. Contrary to existing meta-evaluation, we discover that different evaluators (i.e., combinations of LLMs and prompts) exhibit distinct performance on LLMBAR and even the highestscoring ones have substantial room for improvement. We also present a novel suite of prompting strategies that further close the gap between LLM and human evaluators. With LLMBAR, we hope to offer more insight into LLM evaluators and foster future research in developing better instruction-following models. | EVALUATING LARGE LANGUAGE MODELS AT EVALUATING INSTRUCTION FOLLOWING |
d238583580 | Fine-tuning large pretrained language models on downstream tasks has become the de-facto learning paradigm in NLP.However, conventional approaches finetune all the parameters of the pretrained model, which becomes prohibitive as the model size and the number of tasks grow.Recent work has proposed a variety of parameter-efficient transfer learning methods that only fine-tune a small number of (extra) parameters to attain strong performance.While effective, the critical ingredients for success and the connections among the various methods are poorly understood.In this paper, we break down the design of state-of-the-art parameter-efficient transfer learning methods and present a unified framework that establishes connections between them.Specifically, we re-frame them as modifications to specific hidden states in pretrained models, and define a set of design dimensions along which different methods vary, such as the function to compute the modification and the position to apply the modification.Through comprehensive empirical studies across machine translation, text summarization, language understanding, and text classification benchmarks, we utilize the unified view to identify important design choices in previous methods.Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks. 1 * Equal Contribution.Order determined by random dice rolling. | TOWARDS A UNIFIED VIEW OF PARAMETER-EFFICIENT TRANSFER LEARNING |
d235613427 | We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers. Our network model consists of a set of disjoint subnetworks, with a single hub and multiple workers; further, workers may have different operating rates. The hubs exchange information with one another via a connected, but not necessarily complete, communication network. In our algorithm, sub-networks execute a distributed SGD algorithm, using a hub-and-spoke paradigm, and the hubs periodically average their models with neighboring hubs. We first provide a unified mathematical framework that describes the Multi-Level Local SGD algorithm. We then present a theoretical analysis of the algorithm; our analysis shows the dependence of the convergence error on the worker node heterogeneity, hub network topology, and the number of local, sub-network, and global iterations. We illustrate the effectiveness of our algorithm in a multi-level network with slow workers via simulation-based experiments.Published as a conference paper at ICLR 2021 signed to improve data aggregation and analysis in wireless sensor networks, autonomous vehicles, power systems, and more(Bonomi et al., 2012;Laboratory, 2017; Satyanarayanan, 2017).Motivated by these observations, we propose Multi-Level Local SGD (MLL-SGD), a distributed learning algorithm for heterogeneous multi-level networks. Specifically, we consider a two-level network structure. The lower level consists of a disjoint set of hub-and-spoke sub-networks, each with a single hub server and a set of workers. The upper level network consists of a connected, but not necessarily complete, hub network by which the hubs communicate. For example, in a Fog Computing application, the sub-network workers may be edge devices connected to their local data center, and the data centers act as hubs communicating over a decentralized network. Each subnetwork runs one or more Local SGD rounds, in which its workers train for a local training period, followed by model averaging at the sub-network's hub. Periodically, the hubs average their models with neighbors in the hub network. We model heterogeneous workers using a stochastic approach; each worker executes a local training iteration in each time step with a probability proportional to its computational resources. Thus, different workers may take different numbers of gradient steps within each local training period. Note since MLL-SGD averages every local training period, regardless of how many gradient steps each worker takes, slow workers do not slow algorithm execution.We prove the convergence of MLL-SGD for smooth and potentially non-convex loss functions. We assume data is distributed in an IID manner to all workers. Further, we analyze the relationship between the convergence error and algorithm parameters and find that, for a fixed step size, the error is quadratic in the number of local training iterations and the number of sub-network training iterations, and linear in the average worker operating rate. Our algorithm and analysis are general enough to encompass several variations of SGD as special cases, including classical SGD (Amari, 1993), SGD with weighted workers (McMahan et al., 2017), and Decentralized Local SGD with an arbitrary hub communication network (Wang & Joshi, 2018). Our work provides novel analysis of a distributed learning algorithm in a multi-level network model with heterogeneous workers.Published as a conference paper at ICLR 2021 significantly from that in MLL-SGD in that as the model parameters are partitioned vertically across multiple hubs, and workers communicate with every hub.Several recent works analyze Hierarchical Local SGD (HL-SGD), an algorithm for training a model in a hierarchical network. Different from MLL-SGD, HL-SGD assumes the hub network topology is a hub-and-spoke and also that workers are homogeneous. Zhou & Cong(2019)and Liu et al. (2020) analyze the convergence error of HL-SGD, while Abad et al. (2020) analyzes convergence time. Unlike HL-SGD, MLL-SGD accounts for an arbitrary hub communication graph, and MLL-SGD algorithm execution does not slow down in the presence of heterogeneous worker operating rates. Several other works seek to encapsulate many variations of SGD under a single framework. Koloskova et al. (2020) created a generalized model that considers a gossip-based decentralized SGD algorithm where the communication network is time-varying. However, this work does not account for a multi-level network model nor worker heterogeneity. Wang et al. introduced the Cooperative SGD framework (Wang & Joshi, 2018), a model that includes communication reduction through local SGD steps and decentralized mixing between homogeneous workers. Cooperative SGD also allows for auxiliary variables. These auxiliary variables can be used to model SGD in a multi-level network, but only when sub-network averaging is immediately followed by hubs averaging with their neighbors in the hub network. Our model is more general; it considers heterogeneous workers and it allows for an arbitrary number of averaging rounds within each sub-network between averaging rounds across sub-networks, which is more practical in multi-level networks where inter-hub communication is slow or costly.SYSTEM MODEL AND PROBLEM FORMULATIONIn this section, we introduce our system model, the objective function that we seek to minimize, and the assumptions we make about the function.We consider a set of D sub-networks D = {1, . . . , D}. Each sub-network d ∈ D has a single hub and a set of workers M (d) , with | M (d) | = N (d) . Workers in M (d) only communicate with their own hub and not with any other workers or hubs. We define the set of all workers in the system as M = | MULTI-LEVEL LOCAL SGD: DISTRIBUTED SGD FOR HETEROGENEOUS HIERARCHICAL NETWORKS |
d252780443 | Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives -two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups. Finally, by scaling our model up to 20B parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 (published paper results) on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization. On zero-shot MMLU, UL2 20B outperforms T0 and T5 models. Additionally, we show that UL2 20B works well with chain-of-thought prompting and reasoning, making it an appealing choice for research into reasoning at a small to medium scale of 20B parameters. Finally, we apply FLAN instruction tuning to the UL2 20B model, achieving MMLU and Big-Bench scores competitive to FLAN-PaLM 62B. We release Flax-based T5X model checkpoints for the UL2 20B model and Flan-UL2 20B model at https://github.com/google-research/google-research/tree/master/ul2. * Yi and Mostafa are co-leads of this project and are denoted with | UL2: Unifying Language Learning Paradigms |
d265038030 | Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator. The penalty guarantees the generalization and convergence of GANs. Experiments on synthetic and large scale datasets verify our theoretical analysis.Published as a conference paper at ICLR 2019 2. We show that the original GAN objective encourages gradient exploding in the discriminator. Gradient exploding in the discriminator can lead to mode collapse in the generator.3. We propose a zero-centered gradient penalty (0-GP) for improving the generalization capability of the discriminator. We show that non-zero centered GP and the zero-centered GP proposed inMescheder et al. (2018)cannot make the discriminator generalize. Our 0-GP helps GANs to converge to generalizable equilibria. Theoretical results are verified on real world datasets.4. We show that 0-GP helps the discriminator to distribute its capacity more equally between regions of the space, effectively preventing mode collapse. Experiments on synthetic and real world datasets verify that 0-GP can prevent mode collapse. GANs with 0-GP is much more robust to changes in hyper parameters, optimizers, and network architectures than the original GAN and GANs with other gradient penalties. | IMPROVING GENERALIZATION AND STABILITY OF GENERATIVE ADVERSARIAL NETWORKS |
d253238010 | Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses concerns to scientists who are to deploy these models in scientific analysis and experiments. This work proposes a general mechanism, learnable randomness injection (LRI), which allows building inherently interpretable models based on general GDL backbones. LRI-induced models, once trained, can detect the points in the point cloud data that carry information indicative of the prediction label. We also propose four datasets from real scientific applications that cover the domains of high-energy physics and biochemistry to evaluate the LRI mechanism. Compared with previous post-hoc interpretation methods, the points detected by LRI align much better and stabler with the ground-truth patterns that have actual scientific meanings. LRI is grounded by the information bottleneck principle, and thus LRI-induced models are also more robust to distribution shifts between training and test scenarios. Our code and datasets are available at https://github.com/Graph-COM/LRI. OS AbouZeid, et al. Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc. Physics Letters B, 2012. Alessandro Achille and Stefano Soatto. Emergence of invariance and disentanglement in deep representations. , et al. A common tracking software project. | INTERPRETABLE GEOMETRIC DEEP LEARNING VIA LEARNABLE RANDOMNESS INJECTION |
d256503575 | Visual imitation learning enables reinforcement learning agents to learn to behave from expert visual demonstrations such as videos or image sequences, without explicit, well-defined rewards. Previous research either adopted supervised learning techniques or induce simple and coarse scalar rewards from pixels, neglecting the dense information contained in the image demonstrations. In this work, we propose to measure the expertise of various local regions of image samples, or called patches, and recover multi-dimensional patch rewards accordingly. Patch reward is a more precise rewarding characterization that serves as a finegrained expertise measurement and visual explainability tool. Specifically, we present Adversarial Imitation Learning with Patch Rewards (PatchAIL), which employs a patch-based discriminator to measure the expertise of different local parts from given images and provide patch rewards. The patch-based knowledge is also used to regularize the aggregated reward and stabilize the training. We evaluate our method on DeepMind Control Suite and Atari tasks. The experiment results have demonstrated that PatchAIL outperforms baseline methods and provides valuable interpretations for visual demonstrations. Our codes are available at https://github.com/sail-sg/PatchAIL. | VISUAL IMITATION LEARNING WITH PATCH REWARDS |
d208909759 | The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once. For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable. In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation. This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input. By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks. We demonstrate its applicability to both contextual and gradientbased meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult. Our approach substantially outperforms standard meta-learning algorithms in these settings. | META-LEARNING WITHOUT MEMORIZATION |
d255340832 | The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modality tasks like image classification. Indeed, it is particularly difficult to augment each modality while preserving the overall semantic structure of the data; for example, a caption may no longer be a good description of an image after standard augmentations have been applied, such as translation. Moreover, it is challenging to specify reasonable transformations that are not tailored to a particular modality. In this paper, we introduce LeMDA, Learning Multimodal Data Augmentation, an easy-to-use method that automatically learns to jointly augment multimodal data in feature space, with no constraints on the identities of the modalities or the relationship between modalities. We show that LeMDA can (1) profoundly improve the performance of multimodal deep learning architectures, (2) apply to combinations of modalities that have not been previously considered, and (3) achieve state-of-the-art results on a wide range of applications comprised of image, text, and tabular data. | LEARNING MULTIMODAL DATA AUGMENTATION IN FEATURE SPACE |
d211068995 | We consider the large-scale query-document retrieval problem: given a query (e.g., a question), return the set of relevant documents (e.g., paragraphs containing the answer) from a large document corpus. This problem is often solved in two steps. The retrieval phase first reduces the solution space, returning a subset of candidate documents. The scoring phase then re-ranks the documents. Critically, the retrieval algorithm not only desires high recall but also requires to be highly efficient, returning candidates in time sublinear to the number of documents. Unlike the scoring phase witnessing significant advances recently due to the BERT-style pre-training tasks on cross-attention models, the retrieval phase remains less well studied. Most previous works rely on classic Information Retrieval (IR) methods such as BM-25 (token matching + TF-IDF weights). These models only accept sparse handcrafted features and can not be optimized for different downstream tasks of interest. In this paper, we conduct a comprehensive study on the embedding-based retrieval models. We show that the key ingredient of learning a strong embedding-based Transformer model is the set of pre-training tasks. With adequately designed paragraph-level pre-training tasks, the Transformer models can remarkably improve over the widely-used BM-25 as well as embedding models without Transformers. The paragraph-level pre-training tasks we studied are Inverse Cloze Task (ICT), Body First Selection (BFS), Wiki Link Prediction (WLP), and the combination of all three. * work performed when interning at Google. | PRE-TRAINING TASKS FOR EMBEDDING-BASED LARGE-SCALE RETRIEVAL |
d219530758 | Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To address this, we present a method for quantifying disentanglement that only uses the generative model, by measuring the topological similarity of conditional submanifolds in the learned representation. This method showcases both unsupervised and supervised variants. To illustrate the effectiveness and applicability of our method, we empirically evaluate several state-of-the-art models across multiple datasets. We find that our method ranks models similarly to existing methods.Real ShapePos-y Generated Interpolations GeneratedFigure 1: Factors in the dSprites dataset displaying topological similarity and semantic correspondence to respective latent dimensions in a disentangled generative model, as shown through Wasserstein RLT distributions of homology and latent interpolations along respective dimensions. | Evaluating the Disentanglement of Deep Generative Models through Manifold Topology |
d86393936 | We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner. | SPECTRAL INFERENCE NETWORKS: UNIFYING DEEP AND SPECTRAL LEARNING |
d252872842 | For generative autoencoders to learn a meaningful latent representation for data generation, a careful balance must be achieved between reconstruction error and how close the distribution in the latent space is to the prior. However, this balance is challenging to achieve due to a lack of criteria that work both at the mini-batch (local) and aggregated posterior (global) level. Goodness of fit (GoF) hypothesis tests provide a measure of statistical indistinguishability between the latent distribution and a target distribution class. In this work, we develop the Goodness of Fit Autoencoder (GoFAE), which incorporates hypothesis tests at two levels. At the mini-batch level, it uses GoF test statistics as regularization objectives. At a more global level, it selects a regularization coefficient based on higher criticism, i.e., a test on the uniformity of the local GoF p-values. We justify the use of GoF tests by providing a relaxed L 2 -Wasserstein bound on the distance between the latent distribution and target prior. We propose to use GoF tests and prove that optimization based on these tests can be done with stochastic gradient (SGD) descent on a compact Riemannian manifold. Empirically, we show that our higher criticism parameter selection procedure balances reconstruction and generation using mutual information and uniformity of p-values respectively. Finally, we show that GoFAE achieves comparable FID scores and mean squared errors with competing deep generative models while retaining statistical indistinguishability from Gaussian in the latent space based on a variety of hypothesis tests. | AUTO-ENCODING GOODNESS OF FIT |
d247222625 | A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is focused on capturing the word co-occurrences in a document and hence often suffers from poor performance in analyzing short documents. In addition, its parameter estimation often relies on approximate posterior inference that is either not scalable or suffering from large approximation error. This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space. Embedding the words and topics in the same vector space, we define a method to measure the semantic difference between the embedding vectors of the words of a document and these of the topics, and optimize the topic embeddings to minimize the expected difference over all documents. Experiments on text analysis demonstrate that the proposed method, which is amenable to mini-batch stochastic gradient descent based optimization and hence scalable to big corpora, provides competitive performance in discovering more coherent and diverse topics and extracting better document representations. * Equal contribution. K k=1θ jk = 1 , we can rewrite the CT loss in Eq. 5 as 1 J J j=1 CT(P j , Q j ) = 1 J J j=1 | REPRESENTING MIXTURES OF WORD EMBEDDINGS WITH MIXTURES OF TOPIC EMBEDDINGS |
d54434799 | Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features.In this paper, we introduce a new technique, Quantized Bottleneck Insertion, to learn finite representations of these vectors and features. The result is a quantized representation of the RNN that can be analyzed to improve our understanding of memory use and general behavior. We present results of this approach on synthetic environments and six Atari games. The resulting finite representations are surprisingly small in some cases, using as few as 3 discrete memory states and 10 observations for a perfect Pong policy. We also show that these finite policy representations lead to improved interpretability. | LEARNING FINITE STATE REPRESENTATIONS OF RECURRENT POLICY NETWORKS |
d226281876 | Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents?(2)Within planning, what choices drive performance?(3)To what extent does planning improve generalization? To answer these questions, we study the performance of MuZero [51], a state-of-the-art MBRL algorithm, under a number of interventions and ablations and across a wide range of environments including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) The primary benefit of planning is in driving policy learning. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research. | ON THE ROLE OF PLANNING IN MODEL-BASED DEEP REINFORCEMENT LEARNING |
d220920191 | Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on crosslingual NLP tasks. However, existing approaches essentially capture the cooccurrence among tokens through involving the masked language model (MLM) objective with token-level cross entropy. In this work, we extend these approaches to learn sentence-level representations, and show the effectiveness on cross-lingual understanding and generation. We propose Hierarchical Contrastive Learning (HICTL) to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence. We conduct evaluations on three benchmarks: language understanding tasks (QQP, QNLI, SST-2, MRPC, STS-B and MNLI) in the GLUE benchmark, cross-lingual natural language inference (XNLI) and machine translation. Experimental results show that the HICTL obtains an absolute gain of 1.0%/2.2% accuracy on GLUE/XNLI as well as achieves substantial improvements of +1.7∼+3.6 BLEU on both the highresource and low-resource English→X translation tasks over strong baselines. We will release the source codes as soon as possible. | ON LEARNING UNIVERSAL REPRESENTATIONS ACROSS LANGUAGES |
d246634506 | There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-explored, given the two-fold fundamental challenges: 1) the inter-connection among nodes in one graph, which induces non-IID generation of data points even under the same environment, and 2) the structural information in the input graph, which is also informative for prediction. In this paper, we formulate the OOD problem for node-level prediction on graphs and develop a new domain-invariant learning approach, named Explore-to-Extrapolate Risk Minimization, that facilitates GNNs to leverage invariant graph features for prediction. The key difference to existing invariant models is that we design multiple context explorers (specified as graph editers in our case) that are adversarially trained to maximize the variance of risks from multiple virtual environments. Such a design enables the model to extrapolate from a single observed environment which is the common case for node-level prediction. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution and further demonstrate its power on various real-world datasets for handling distribution shifts from artificial spurious features, cross-domain transfers and dynamic graph evolution.Recent studies of the OOD generalization problem likeRojas-Carulla et al. (2018); Bühlmann (2018); | HANDLING DISTRIBUTION SHIFTS ON GRAPHS: AN INVARIANCE PERSPECTIVE |
d263671952 | Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic policies to sequentially generate compositional objects from a given unnormalized reward distribution.They can generate diverse sets of high-reward objects, which is an important consideration in scientific discovery tasks.However, as they are typically trained from a given extrinsic reward function, it remains an important open challenge about how to leverage the power of pre-training and train GFlowNets in an unsupervised fashion for efficient adaptation to downstream tasks.Inspired by recent successes of unsupervised pre-training in various domains, we introduce a novel approach for reward-free pre-training of GFlowNets.By framing the training as a self-supervised problem, we propose an outcome-conditioned GFlowNet (OC-GFN) that learns to explore the candidate space.Specifically, OC-GFN learns to reach any targeted outcomes, akin to goal-conditioned policies in reinforcement learning.We show that the pre-trained OC-GFN model can allow for a direct extraction of a policy capable of sampling from any new reward functions in downstream tasks.Nonetheless, adapting OC-GFN on a downstream task-specific reward involves an intractable marginalization over possible outcomes.We propose a novel way to approximate this marginalization by learning an amortized predictor enabling efficient fine-tuning.Extensive experimental results validate the efficacy of our approach, demonstrating the effectiveness of pre-training the OC-GFN, and its ability to swiftly adapt to downstream tasks and discover modes more efficiently.This work may serve as a foundation for further exploration of pre-training strategies in the context of GFlowNets. | Pre-Training and Fine-Tuning Generative Flow Networks |
d263830894 | We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with nonindependent sampling strategies that promote spatial coverage.In particular, we propose an easily implemented method based on the pivotal sampling algorithm, which we test on problems motivated by learning-based methods for parametric PDEs and uncertainty quantification.In comparison to independent sampling, our method reduces the number of samples needed to reach a given target accuracy by up to 50%.We support our findings with two theoretical results.First, we show that any non-independent leverage score sampling method that obeys a weak onesided ℓ ∞ independence condition (which includes pivotal sampling) can actively learn d dimensional linear functions with O(d log d) samples, matching independent sampling.This result extends recent work on matrix Chernoff bounds under ℓ ∞ independence, and may be of interest for analyzing other sampling strategies beyond pivotal sampling.Second, we show that, for the important case of polynomial regression, our pivotal method obtains an improved bound of O(d) samples. | IMPROVED ACTIVE LEARNING VIA DEPENDENT LEVERAGE SCORE SAMPLING |
d233287919 | The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ordering by designing problem-specific loss functions and pre-training with an ordering pre-selected. Other recent works use iterative search to discover problemspecific orderings for training, but suffer from high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data-no domain knowledge required. The learner contains an encoder network and decoder language model that perform variational inference with autoregressive orders (represented as permutation matrices) as latent variables. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Permutations then serve as target generation orders for training an insertionbased Transformer language model. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders. * Authors contributed equally. | DISCOVERING NON-MONOTONIC AUTOREGRESSIVE ORDERINGS WITH VARIATIONAL INFERENCE |
d257232422 | Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry. This, however, often comes with high computational costs, which raises the question if one can learn an efficient neural network approximation. We show that this is indeed possible for shape spaces with a special product structure, namely those smoothly approximable by a direct sum of low-dimensional manifolds. Our proposed architecture leverages this structure by separately learning approximations for the low-dimensional factors and a subsequent combination. After developing the approach as a general framework, we apply it to a shape space of triangular surfaces. Here, typical examples of data manifolds are given through datasets of articulated models and can be factorized, for example, by a Sparse Principal Geodesic Analysis (SPGA). We demonstrate the effectiveness of our proposed approach with experiments on synthetic data as well as manifolds extracted from data via SPGA. ζ 1 (v 1 ), . . . , ψ ζ J (v J )), where Ψ ζ is a NN and the ψ ζ j are further NNs approximating the Riemannian exponential exp z on the low-dimensional factor manifolds.We develop our approach focusing on the shape space of discrete shells, where shapes are given by triangle meshes and the manifold is equipped with an elasticity-based metric. In principle, our approach is also applicable to other shape spaces such as manifolds of images, and we will include remarks on how we propose this could work. We evaluate our approach with experiments on data manifolds of triangle meshes, both synthetic ones and ones extracted from data via SPGA, and we 1 | PARAMETRIZING PRODUCT SHAPE MANIFOLDS BY COMPOSITE NETWORKS |
d252668917 | Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede open access to such models. To democratize this, we train and release a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER. We show the utility of the trained model by demonstrating that it is competitive with the previous state-of-the-art on zero-shot Python code generation on HumanEval. We further investigate the multi-step paradigm for program synthesis, where a single program is factorized into multiple prompts specifying subproblems. To this end, we construct an open benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse problem sets that are factorized into multi-turn prompts. Our analysis on MTPB shows that the same intent provided to CODEGEN in multiturn fashion significantly improves program synthesis over that provided as a single turn. We make the training library JAXFORMER and model checkpoints available as open source contribution: https://github.com/salesforce/CodeGen. | CODEGEN: AN OPEN LARGE LANGUAGE MODEL FOR CODE WITH MULTI-TURN PROGRAM SYNTHESIS |
d232170613 | Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that maximum entropy (MaxEnt) RL maximizes a lower bound on a robust RL objective, and thus can be used to learn policies that are robust to some disturbances in the dynamics and the reward function. While this capability of MaxEnt RL has been observed empirically in prior work, to the best of our knowledge our work provides the first rigorous proof and theoretical characterization of the MaxEnt RL robust set. While a number of prior robust RL algorithms have been designed to handle similar disturbances to the reward function or dynamics, these methods typically require additional moving parts and hyperparameters on top of a base RL algorithm. In contrast, our results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications. While this does not imply that MaxEnt RL is the best available robust RL method, MaxEnt RL is a simple robust RL method with appealing formal guarantees.arXiv:2103.06257v2 [cs.LG] 5 May 2022Published as a conference paper at ICLR 2022 are robust. Showing how to obtain robust policies from existing MaxEnt RL methods, which already constitute a significant portion of the RL methods in use today(Abdolmaleki et al., 2018;Haarnoja et al., 2018a; Vieillard et al., 2020), would be useful because it would enable practitioners to leverage existing, tried-and-true methods to solve robust RL problems.Stochastic policies, of the sort learned with MaxEnt RL, inject noise into the actions during training, thereby preparing themselves for deployment in environments with disturbances. For example, in the robot pushing task shown inFig. 1, the policy learned by MaxEnt RL pushes the white puck to the goal using many possible routes. In contrast, (standard) RL learns a deterministic policy, always using the same route to get to the goal. Now, imagine that this environment is perturbed by adding the red barrier inFig. 1. While the policy learned by (standard) RL always collides with this obstacle, the policy learned by MaxEnt RL uses many routes to solve the task, and some fraction of these routes continue to solve the task even when the obstacle is present. While a number of prior works have articulated the intuition that the stochastic policies learned via MaxEnt RL should be robust to disturbances (Levine, 2018;Abdolmaleki et al., 2018;Lee et al., 2019), no prior work has actually shown that MaxEnt RL policies are provably robust, nor characterized the set of disturbances to which they are robust. Applications of MaxEnt RL methods to problems that demand robustness are likely hampered by a lack of understanding of when such methods are robust, what kinds of reward functions should be used to obtain the desired type of robustness, and how the task should be set up. The goal in our work is to make this notion precise, proving that MaxEnt RL is already a robust RL algorithm, and deriving the robust set for these policies. | MAXIMUM ENTROPY RL (PROVABLY) SOLVES SOME ROBUST RL PROBLEMS |
d264426077 | Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed.Modern deep learning models have been brought to bear for these tasks too, as in the framework of plug-and-play or deep unrolling, where they loosely resemble proximal operators.Yet, something essential is lost in employing these purely data-driven approaches: there is no guarantee that a general deep network represents the proximal operator of any function, nor is there any characterization of the function for which the network might provide some approximate proximal.This not only makes guaranteeing convergence of iterative schemes challenging but, more fundamentally, complicates the analysis of what has been learned by these networks about their training data.Herein we provide a framework to develop learned proximal networks (LPN), prove that they provide exact proximal operators for a data-driven nonconvex regularizer, and show how a new training strategy, dubbed proximal matching, provably promotes the recovery of the log-prior of the true data distribution.Such LPN provide general, unsupervised, expressive proximal operators that can be used for general inverse problems with convergence guarantees.We illustrate our results in a series of cases of increasing complexity, demonstrating that these models not only result in state-of-the-art performance, but provide a window into the resulting priors learned from data. | What's in a Prior? Learned Proximal Networks for Inverse Problems |
d52911374 | Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations-and for instructions-not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples. * Work done during an internship at DeepMind. † Now at Facebook AI Research. | LEARNING TO UNDERSTAND GOAL SPECIFICATIONS BY MODELLING REWARD |
d238419650 | With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as the loss for training GANs. In contrast to this approach, we show that the OT map itself can be used as a generative model, providing comparable performance. Previous analogous approaches consider OT maps as generative models only in the latent spaces due to their poor performance in the original high-dimensional ambient space. In contrast, we apply OT maps directly in the ambient space, e.g., a space of high-dimensional images. First, we derive a minmax optimization algorithm to efficiently compute OT maps for the quadratic cost (Wasserstein-2 distance). Next, we extend the approach to the case when the input and output distributions are located in the spaces of different dimensions and derive error bounds for the computed OT map. We evaluate the algorithm on image generation and unpaired image restoration tasks. In particular, we consider denoising, colorization, and inpainting, where the optimality of the restoration map is a desired attribute, since the output (restored) image is expected to be close to the input (degraded) one.Published as a conference paper at ICLR 2022 (a) OT cost as the loss for the generative model. (b) OT map as the generative model. DC Dowson and BV Landau. The fréchet distance between multivariate normal distributions. Journal of multivariate analysis, 12(3):450-455, 1982.Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689, 2019. | GENERATIVE MODELING WITH OPTIMAL TRANSPORT MAPS |
d9128667 | Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images require the predictive model to build an intricate understanding of the natural world. Many existing methods tackle this problem by making simplifying assumptions about the environment. One common assumption is that the outcome is deterministic and there is only one plausible future. This can lead to low-quality predictions in real-world settings with stochastic dynamics. In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables. To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world videos. We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned. We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods. Our SV2P implementation will be open sourced upon publication. | STOCHASTIC VARIATIONAL VIDEO PREDICTION |
d257219444 | The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method.From a distributional view, MLE in fact minimizes the Kullback-Leibler divergence (KLD) between the distribution of the real data and that of the model.However, this approach forces the model to distribute non-zero (sometimes large) probability mass to all training samples regardless of their quality.Moreover, in the attempt to cover the low-probability regions in the data distribution, the model systematically overestimates the probability of corrupted text sequences, which we conjecture is one of the main reasons for text degeneration during autoregressive decoding.To remedy this problem, we leverage the total variation distance (TVD) with its robustness to outliers, and develop practical bounds to apply it to language generation.Then, we introduce the TaiLr 1 objective that balances the tradeoff of estimating TVD.Intuitively, TaiLr downweights real data samples that have low model probabilities with tunable penalization intensity.Experimental results show that our method alleviates the overestimation of degenerated sequences without sacrificing diversity and improves generation quality on a wide range of text generation tasks. 2 * Corresponding Author. 1 Pronounced as "tailor". | TAILORING LANGUAGE GENERATION MODELS UNDER TOTAL VARIATION DISTANCE |
d223953610 | Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. For this study, we collect a dataset of human interactions capturing body part movements and gaze in their daily lives. Our experiments show that our self-supervised representation that encodes interaction and attention cues outperforms a visual-only state-of-the-art method MoCo (He et al., 2020), on a variety of target tasks: scene classification (semantic), action recognition (temporal), depth estimation (geometric), dynamics prediction (physics) and walkable surface estimation (affordance).Figure 1: We propose to use human's interactions with their visual surrounding as a training signal for self-supervised representation learning. We record first person observations as well as the movements and gaze of people living their daily routines and use these cues to learn a visual embedding. We use the learned representation on a variety of diverse tasks and show consistent improvements compared to state-of-the-art self-supervised vision-only techniques. | What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions |
d3508167 | Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves new state-of-the-art results. In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new "super-separable" convolution operation that further reduces the number of parameters and computational cost for obtaining state-of-the-art results. * All authors contributed equally and are ordered randomly. † Work performed while at Google Brain. | Depthwise Separable Convolutions for Neural Machine Translation |
d108306873 | We present the perceptor gradients algorithm -a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions. We show that the proposed algorithm is able to learn representations that can be directly fed into a Linear-Quadratic Regulator (LQR) or a general purpose A* planner. Our experimental results confirm that the perceptor gradients algorithm is able to efficiently learn transferable symbolic representations as well as generate new observations according to a semantically meaningful specification. | LEARNING PROGRAMMATICALLY STRUCTURED REPRESENTATIONS WITH PERCEPTOR GRADIENTS |
d242784472 | Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization-one of the most popular algorithms for sim-to-real transfer-has been demonstrated to be effective in various tasks in robotics and autonomous driving. Despite its empirical successes, theoretical understanding on why this simple algorithm works is limited. In this paper, we propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters (corresponding to unknown physical parameters such as friction). We provide sharp bounds on the sim-to-real gap-the difference between the value of policy returned by domain randomization and the value of an optimal policy for the real world. We prove that sim-to-real transfer can succeed under mild conditions without any real-world training samples. Our theory also highlights the importance of using memory (i.e., history-dependent policies) in domain randomization. Our proof is based on novel techniques that reduce the problem of bounding the sim-to-real gap to the problem of designing efficient learning algorithms for infinite-horizon MDPs, which we believe are of independent interest. * These two authors contributed equally. Shipra Agrawal and Randy Jia. Posterior sampling for reinforcement learning: worst-case regret bounds. arXiv preprint arXiv:1705.07041, 2017. , et al. Using simulation and domain adaptation to improve efficiency of deep robotic grasping. In 2018 IEEE international conference on robotics and automation (ICRA), pp. 4243-4250. IEEE, 2018. Peter Buchholz and Dimitri Scheftelowitsch. Computation of weighted sums of rewards for concurrent MDPs. . Transfer from simulation to real world through learning deep inverse dynamics model. arXiv preprint arXiv:1610.03518, 2016.Mark Cutler and Jonathan P How. Efficient reinforcement learning for robots using informative simulated priors. . Sim2real transfer learning for 3d human pose estimation: motion to the rescue. | UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER |
d253117027 | Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning.While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques.In this work, we show that a steganography procedure is perfectly secure under Cachin (1998)'s information-theoretic model of steganography if and only if it is induced by a coupling.Furthermore, we show that, among perfectly secure procedures, a procedure maximizes information throughput if and only if it is induced by a minimum entropy coupling.These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees for arbitrary covertext distributions.To provide empirical validation, we compare a minimum entropy coupling-based approach to three modern baselines-arithmetic coding, Meteor, and adaptive dynamic groupingusing GPT-2, WaveRNN, and Image Transformer as communication channels.We find that the minimum entropy coupling-based approach achieves superior encoding efficiency, despite its stronger security constraints.In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling. | PERFECTLY SECURE STEGANOGRAPHY USING MINIMUM ENTROPY COUPLING |
d259286823 | Hypernetworks, neural networks that predict the parameters of another neural network, are powerful models that have been successfully used in diverse applications from image generation to multi-task learning. Unfortunately, existing hypernetworks are often challenging to train. Training typically converges far more slowly than for non-hypernetwork models, and the rate of convergence can be very sensitive to hyperparameter choices. In this work, we identify a fundamental and previously unidentified problem that contributes to the challenge of training hypernetworks: a magnitude proportionality between the inputs and outputs of the hypernetwork. We demonstrate both analytically and empirically that this can lead to unstable optimization, thereby slowing down convergence, and sometimes even preventing any learning. We present a simple solution to this problem using a revised hypernetwork formulation that we call Magnitude Invariant Parametrizations (MIP). We demonstrate the proposed solution on several hypernetwork tasks, where it consistently stabilizes training and achieves faster convergence. Furthermore, we perform a comprehensive ablation study including choices of activation function, normalization strategies, input dimensionality, and hypernetwork architecture; and find that MIP improves training in all scenarios. We provide easyto-use code that can turn existing networks into MIP-based hypernetworks. | Magnitude Invariant Parametrizations Improve Hypernetwork Learning |
d252544798 | Error correction code (ECC) is an integral part of the physical communication layer, ensuring reliable data transfer over noisy channels. Recently, neural decoders have demonstrated their advantage over classical decoding techniques. However, recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders. In this work, we propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths. Our framework models the forward channel corruption as a series of diffusion steps that can be reversed iteratively. Three contributions are made: (i) a diffusion process suitable for the decoding setting is introduced, (ii) the neural diffusion decoder is conditioned on the number of parity errors, which indicates the level of corruption at a given step, (iii) a line search procedure based on the code's syndrome obtains the optimal reverse diffusion step size. The proposed approach demonstrates the power of diffusion models for ECC and is able to achieve state of the art accuracy, outperforming the other neural decoders by sizable margins, even for a single reverse diffusion step.arXiv:2209.13533v1 [cs.IT] 16 Sep 2022Beyond the conceptual novelty, we make three technical contributions: (i) our framework is based on an adapted diffusion process that simulates the coding and transmission processes, (ii) we further condition the denoising model on the number of parity-check errors, as an indicator of the signal's level of corruption, and (iii) we propose a line-search procedure that minimizes the denoised code syndrome, in order to provide an optimal step size for the reverse diffusion.Applied to a wide variety of codes, our method outperforms the state-of-the-art learning-based solutions by very large margins, employing extremely shallow architectures. Furthermore, we show that even a single reverse diffusion step with a controlled step size can outperform concurrent methods.RELATED WORKSThe emergence of deep learning for communication and information theory applications has demonstrated the advantages of neural networks in many tasks, such as channel equalization, modulation, detection, quantization, compression, and decoding (Ibnkahla, 2000). Model-free decoders employ general neural network architectures (Cammerer et al. | DENOISING DIFFUSION ERROR CORRECTION CODES |
d246411225 | We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coordinates not only helps using explicit positional priors to improve the queryto-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer-by-layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods. | DAB-DETR: DYNAMIC ANCHOR BOXES ARE BETTER QUERIES FOR DETR |
d259108693 | Learning features from data is one of the defining characteristics of deep learning, but our theoretical understanding of the role features play in deep learning is still rudimentary. To address this gap, we introduce a new tool, the interaction tensor, for empirically analyzing the interaction between data and model through features. With the interaction tensor, we make several key observations about how features are distributed in data and how models with different random seeds learn different features. Based on these observations, we propose a conceptual framework for feature learning. Under this framework, the expected accuracy for a single hypothesis and agreement for a pair of hypotheses can both be derived in closed-form. We demonstrate that the proposed framework can explain empirically observed phenomena, including the recently discovered Generalization Disagreement Equality (GDE) that allows for estimating the generalization error with only unlabeled data. Further, our theory also provides explicit construction of natural data distributions that break the GDE. Thus, we believe this work provides valuable new insight into our understanding of feature learning.Preprint. Under review.arXiv:2306.04793v1 [cs.LG] 7 Jun 2023Recent works have started to incorporate feature learning into theoretical analysis[37,1,62,31,59,2,3]. This paper is most immediately related to Allen-Zhu and Li [1] who propose the multi-view data structure where there exist two types of data: multi-view data which contain all the features of a class and single-view data which contain only one feature. They showed that a single two-layer CNN will only learn one feature for each class. In this work, we investigate whether this structure of features holds in practice by treating features as first-class citizens in both empirical investigation and theoretical analysis. Our experimental results reveal a more nuanced perspective on the structure of data and features. Based on these observations, we propose an abstract theoretical model that better | On the Joint Interaction of Models, Data, and Features |
d213152193 | Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute. | ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS |
d247244684 | Given two point sets, the problem of registration is to recover a transformation that matches one set to the other. This task is challenging due to the presence of the large number of outliers, the unknown non-rigid deformations and the large sizes of point sets. To obtain strong robustness against outliers, we formulate the registration problem as a partial distribution matching (PDM) problem, where the goal is to partially match the distributions represented by point sets in a metric space. To handle large point sets, we propose a scalable PDM algorithm by utilizing the efficient partial Wasserstein-1 (PW) discrepancy. Specifically, we derive the Kantorovich-Rubinstein duality for the PW discrepancy, and show its gradient can be explicitly computed. Based on these results, we propose a partial Wasserstein adversarial network (PWAN), which is able to approximate the PW discrepancy by a neural network, and minimize it by gradient descent. In addition, it also incorporates an efficient coherence regularizer for non-rigid transformations to avoid unrealistic deformations. We evaluate PWAN on practical point set registration tasks, and show that the proposed PWAN is robust, scalable and performs more favorably than the state-of-the-art methods. | PARTIAL WASSERSTEIN ADVERSARIAL NETWORK FOR NON-RIGID POINT SET REGISTRATION |
d253018433 | A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in the basic setting of multiplayer general-sum games, existing algorithms require a number of samples exponential in the number of players to learn rationalizable equilibria under bandit feedback. This paper develops the first line of efficient algorithms for learning rationalizable Coarse Correlated Equilibria (CCE) and Correlated Equilibria (CE) whose sample complexities are polynomial in all problem parameters including the number of players. To achieve this result, we also develop a new efficient algorithm for the simpler task of finding one rationalizable action profile (not necessarily an equilibrium), whose sample complexity substantially improves over the best existing results of Wu et al. (2021). Our algorithms incorporate several novel techniques to guarantee rationalizability and no (swap-)regret simultaneously, including a correlated exploration scheme and adaptive learning rates, which may be of independent interest. We complement our results with a sample complexity lower bound showing the sharpness of our guarantees. * Equal contribution. ∆ 2 1 samples in normal-form games with N players, A actions per player and a minimum elimination length of L. This greatly improves the result of Wu et al. (2021) and is tight up to logarithmic factors when L = O(1). 1 Throughout this paper, we use O to suppress logarithmic factors in N , A, | Learning Rationalizable Equilibria in Multiplayer Games |
d261493986 | Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models.However, these models fail to generalize past the sequence length they were trained on.We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context window of such models, requiring 10x less tokens and 2.5x less training steps than previous methods.Using YaRN, we show that LLaMA models can effectively utilize and extrapolate to context lengths much longer than their original pre-training would allow, while also surpassing previous the state-of-the-art at context window extension.In addition, we demonstrate that YaRN exhibits the capability to extrapolate beyond the limited context of a fine-tuning dataset.The models fine-tuned using YaRN has been made available and reproduced online up to 128k context length at https://github.com/jquesnelle/yarn. | YaRN: Efficient Context Window Extension of Large Language Models |
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