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d221785486 | Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the geometric and relational aspects of the problem domain. To address this gap, we introduce geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors. Graph neural networks equipped with such layers are able to perform both geometric and relational reasoning on efficient representations of macromolecules. We demonstrate our approach on two important problems in learning from protein structure: model quality assessment and computational protein design. Our approach improves over existing classes of architectures on both problems, including state-ofthe-art convolutional neural networks and graph neural networks. We release our code at https://github.com/drorlab/gvp. | Published as a conference paper at ICLR 2021 LEARNING FROM PROTEIN STRUCTURE WITH GEOMETRIC VECTOR PERCEPTRONS |
d3384895 | The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in the limit of infinite data. In the context of limited and noisy data however, the use of a loss function that is specifically designed for top-k classification can bring significant improvements. Our empirical evidence suggests that the loss function must be smooth and have non-sparse gradients in order to work well with deep neural networks. Consequently, we introduce a family of smoothed loss functions that are suited to top-k optimization via deep learning. The widely used cross-entropy is a special case of our family. Evaluating our smooth loss functions is computationally challenging: a naïve algorithm would require O( n k ) operations, where n is the number of classes. Thanks to a connection to polynomial algebra and a divideand-conquer approach, we provide an algorithm with a time complexity of O(kn). Furthermore, we present a novel approximation to obtain fast and stable algorithms on GPUs with single floating point precision. We compare the performance of the cross-entropy loss and our margin-based losses in various regimes of noise and data size, for the predominant use case of k = 5. Our investigation reveals that our loss is more robust to noise and overfitting than cross-entropy. | Published as a conference paper at ICLR 2018 SMOOTH LOSS FUNCTIONS FOR DEEP TOP-K CLASSIFICATION |
d5462200 | Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. This was shown to speed up training and result in better models. However its success has been very limited when dealing with recurrent neural networks. On the other hand, layer normalization normalizes the activations across all activities within a layer. This was shown to work well in the recurrent setting. In this paper we propose a unified view of normalization techniques, as forms of divisive normalization, which includes layer and batch normalization as special cases. Our second contribution is the finding that a small modification to these normalization schemes, in conjunction with a sparse regularizer on the activations, leads to significant benefits over standard normalization techniques. We demonstrate the effectiveness of our unified divisive normalization framework in the context of convolutional neural nets and recurrent neural networks, showing improvements over baselines in image classification, language modeling as well as super-resolution. * indicates equal contribution | Published as a conference paper at ICLR 2017 NORMALIZING THE NORMALIZERS: COMPARING AND EXTENDING NETWORK NORMALIZATION SCHEMES |
d257631600 | By enabling agents to communicate, recent cooperative multi-agent reinforcement learning (MARL) methods have demonstrated better task performance and more coordinated behavior. Most existing approaches facilitate inter-agent communication by allowing agents to send messages to each other through free communication channels, i.e., cheap talk channels. Current methods require these channels to be constantly accessible and known to the agents a priori. In this work, we lift these requirements such that the agents must discover the cheap talk channels and learn how to use them. Hence, the problem has two main parts: cheap talk discovery (CTD) and cheap talk utilization (CTU). We introduce a novel conceptual framework for both parts and develop a new algorithm based on mutual information maximization that outperforms existing algorithms in CTD/CTU settings. We also release a novel benchmark suite to stimulate future research in CTD/CTU.INTRODUCTIONEffective communication is essential for many multi-agent systems in the partially observable setting, which is common in many real-world applications like elevator control(Crites & Barto, 1998)and sensor networks(Fox et al., 2000). Communicating the right information at the right time becomes crucial to completing tasks effectively. In the multi-agent reinforcement learning (MARL) setting, communication often occurs on free channels known as cheap talk channels. The agents' goal is to learn an effective communication protocol via the channel. The transmitted messages can be either discrete or continuous(Foerster et al., 2016).Existing work often assumes the agents have prior knowledge (e.g., channel capacities and noise level) about these channels. However, such assumptions do not always hold. Even if these channels' existence can be assumed, they might not be persistent, i.e., available at every state. Consider the real-world application of inter-satellite laser communication. In the case, communication channel is only functional when satellites are within line of sight. This means positioning becomes essential(Lakshmi et al., 2008). Thus, Without these assumptions, agents need the capability to discover where to best communicate before learning a protocol in realistic MARL settings.In this work, we investigate the setting where these assumptions on cheap talk channels are lifted. Precisely, these channels are only effective in a subset of the state space. Hence, agents must discover where these channels are before they can learn how to use them. We divide this problem into two sequential steps: cheap talk discovery (CTD) and cheap talk utilization (CTU). The problem is a strict generalization of the common setting used in the emergent communication literature with 1 Published as a conference paper at ICLR 2023 less assumptions, which is more akin to real-world scenarios (see appendix A for more in-depth discussions on the setting's significance and use cases). Figure 1: The two learning stages for CTD/CTU based on PBMaze. Stage (a): Discover the functional phone booths; Stage (b): Form a protocol to use the phone booth and learn to interpret the messages (left), and solve the task (right). The blue and red agents are the sender and the receiver respectivelyThis setting is particularly difficult as it suffers from the temporal credit assignment problem (Sutton, 1984) for communicative actions. Consider an example we call the phone booth maze (PBMaze), the environment has a sender and a receiver, placed into two separate rooms. The receiver's goal is to escape from the correct exit out of two possible exits. Only the sender knows which one is the correct exit. The sender's goal is to communicate this information using functional phone booths.RELATED WORKThe use of mutual information (MI) has been explored in the MARL setting. Wang et al. (2019) propose a shaping reward based on MI between agents' transitions to improve exploration, encouraging visiting critical points where one can influence other agents. Our proposed method also has an MI term for reward shaping. Their measure might behave similarly to ours but is harder to compute and requires full environmental states during training. Sokota et al. (2022) propose a method to discover implicit communication protocols using environment actions via minimum entropy coupling, separating communicative and non-communicative decision-making. We propose a similar problem decomposition by separating state and action spaces into two subsets based on whether communication can occur or not. Unlike in Sokota et al.(2022), we focus on explicit communication , et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484-489, 2016.Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar. Learning when to communicate at scale in multiagent cooperative and competitive . Model-based multi-agent reinforcement learning: Recent progress and prospects. arXiv preprint arXiv:2203.10603, 2022. | Published as a conference paper at ICLR 2023 CHEAP TALK DISCOVERY AND UTILIZATION IN MULTI-AGENT REINFORCEMENT LEARNING |
d4807923 | Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protocols -one grounded in the semantics of the game, and one which is a priori ungrounded and is a form of cheap talk. We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded channel. However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge. We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation. | Published as a conference paper at ICLR 2018 EMERGENT COMMUNICATION THROUGH NEGOTIATION |
d253237390 | Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL and EA to devise new methods by fusing their complementary advantages. However, existing works on combining Deep RL and EA have two common drawbacks: 1) the RL agent and EA agents learn their policies individually, neglecting efficient sharing of useful common knowledge; 2) parameter-level policy optimization guarantees no semantic level of behavior evolution for the EA side. In this paper, we propose Evolutionary Reinforcement Learning with Two-scale State Representation and Policy Representation (ERL-Re 2 ), a novel solution to the aforementioned two drawbacks. The key idea of ERL-Re 2 is two-scale representation: all EA and RL policies share the same nonlinear state representation while maintaining individual linear policy representations. The state representation conveys expressive common features of the environment learned by all the agents collectively; the linear policy representation provides a favorable space for efficient policy optimization, where novel behavior-level crossover and mutation operations can be performed. Moreover, the linear policy representation allows convenient generalization of policy fitness with the help of the Policy-extended Value Function Approximator (PeVFA), further improving the sample efficiency of fitness estimation. The experiments on a range of continuous control tasks show that ERL-Re 2 consistently outperforms advanced baselines and achieves the State Of The Art (SOTA). Our code is available on https://github.com/yeshenpy/ERL-Re2.arXiv:2210.17375v2 [cs.NE] 30 Jun 2023Published as a conference paper at ICLR 2023The population and the RL agent interact with each other in a coherent framework: the RL agent learns by DDPG with diverse off-policy experiences collected by the population; while the population includes a copy of the RL agent periodically among which genetic evolution operates. In this way, EA and RL cooperate during policy optimization. Subsequently, many variants and improvements of ERL are proposed, e.g., to incorporate Cross-Entropy Method (CEM) (Pourchot & Sigaud, 2019) rather than GA (Pourchot & Sigaud, 2019), to devise gradient-based genetic operators (Gangwani & Peng, 2018), to use multiple parallel RL agents (Khadka et al., 2019) and etc. However, we observe that most existing methods seldom break the performance ceiling of either their EA or RL components (e.g., Swimmer and Humanoid on MuJoCo are dominated by EA and RL respectively). This indicates that the strengths of EA and RL are not sufficiently blended. We attribute this to two major drawbacks. First, each agent of EA and RL learns its policy individually. The state representation learned by individuals can inevitably be redundant yet specialized (Dabney et al., 2021), thus slowing down the learning and limiting the convergence performance. Second, typical evolutionary variation occurs at the level of the parameter (e.g., network weights). It guarantees no semantic level of evolution and may induce policy crash (Bodnar et al., 2020).In the literature of linear approximation RL(Sutton & Barto, 1998)and state representation learning (Chung et al., 2019; Dabney et al., 2021; Kumar et al., 2021), a policy is usually understood as the composition of nonlinear state features and linear policy weights. Taking this inspiration, we propose a new approach named Evolutionary Reinforcement Learning with Two-scale State Representation and Policy Representation (ERL-Re 2 ) to address the aforementioned two drawbacks. ERL-Re 2 is devised based on a novel concept, i.e., two-scale representation: all EA and RL agents maintained in ERL-Re 2 are composed of a shared nonlinear state representation and an individual linear policy representation. The shared state representation takes the responsibility of learning general and expressive features of the environment, which is not specific to any single policy, e.g., the common decision-related knowledge. In particular, it is learned by following a unifying update direction derived from value function maximization regarding all EA and RL agents collectively. Thanks to the expressivity of the shared state representation, the individual policy representation can have a simple linear form. It leads to a fundamental distinction of ERL-Re 2 : evolution and reinforcement occur in the linear policy representation space rather than in a nonlinear parameter (e.g., policy network) space as the convention. Thus, policy optimization can be more efficient with ERL-Re 2 . In addition, we propose novel behavior-level crossover and mutation that allow to imposing variations on designated dimensions of action while incurring no interference on the others. Compared to parameter-level operators, our behavior-level operators have clear genetic semantics of behavior, thus are more effective and stable. Moreover, we further reduce the sample cost of EA by introducing a new surrogate of fitness, based on the convenient incorporation of Policy-extended Value Function Approximator (PeVFA) favored by the linear policy representations. Without loss of generality, we use GA and TD3 (and DDPG) for the concrete choices of EA and RL algorithms. Finally, we evaluate ERL-Re 2 on MuJoCo continuous control tasks with strong ERL baselines and typical RL algorithms, along with a comprehensive study on ablation, hyperparameter analysis, etc.We summarize our major contributions below: 1) We propose a novel approach ERL-Re 2 to integrate EA and RL based on the concept of two-scale representation; 2) We devise behavior-level crossover and mutation which have clear genetic semantics; 3) We empirically show that ERL-Re 2 outperforms other related methods and achieves state-of-the-art performance.BACKGROUNDReinforcement Learning Consider a Markov decision process (MDP), defined by a tuple S, A, P, R, γ, T . At each step t, the agent uses a policy π to select an action a t ∼ π(s t ) ∈ A according to the state s t ∈ S and the environment transits to the next state s t+1 according to transition function P(s t , a t ) and the agent receives a reward r t = R(s t , a t ). The return is defined as the discounted cumulative reward, denoted by R t = T i=t γ i−t r i where γ ∈ [0, 1) is the discount factor and T is the maximum episode horizon. The goal of RL is to learn an optimal policy π * that maximizes the expected return. DDPG (Lillicrap et al., 2016) is a representative off-policy Actor-Critic algorithm, consisting of a deterministic policy π ω (i.e., the actor) and a state-action value function approximation Q ψ (i.e., the critic), with the parameters ω and ψ respectively. The critic is optimized with the Temporal Difference (TD)(Sutton & Barto, 1998)loss and the actor is updated by maximizing the estimated Q value. The loss functions are defined as: L(ψ) = E D [(r + γQ ψ (s , π ω (s )) − Q ψ (s, a)) 2 ] and | ERL-RE 2 : EFFICIENT EVOLUTIONARY REINFORCE- MENT LEARNING WITH SHARED STATE REPRESENTA- TION AND INDIVIDUAL POLICY REPRESENTATION |
d50773706 | Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption; (2) most architecture search methods require vast computational resources. We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the entire Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method. We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents. This is accomplished by using (approximate) network morphism operators for generating children. The combination of these two contributions allows finding models that are on par or even outperform both hand-crafted as well as automatically-designed networks. | EFFICIENT MULTI-OBJECTIVE NEURAL ARCHITEC- TURE SEARCH VIA LAMARCKIAN EVOLUTION |
d247446904 | Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://github.com/ DeepGraphLearning/GearNet. | Published as a conference paper at ICLR 2023 PROTEIN REPRESENTATION LEARNING BY GEOMETRIC STRUCTURE PRETRAINING |
d6079627 | Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Machine (RBM), and its staple training algorithm contrastive divergence (CD), have been the prototype for some recent advancements in the unsupervised training of deep neural networks. However, CD has limited theoretical motivation, and can in some cases produce undesirable behavior. Here, we investigate the performance of Minimum Probability Flow (MPF) learning for training RBMs. Unlike CD, with its focus on approximating an intractable partition function via Gibbs sampling, MPF proposes a tractable, consistent, objective function defined in terms of a Taylor expansion of the KL divergence with respect to sampling dynamics. Here we propose a more general form for the sampling dynamics in MPF, and explore the consequences of different choices for these dynamics for training RBMs. Experimental results show MPF outperforming CD for various RBM configurations. | UNDERSTANDING MINIMUM PROBABILITY FLOW FOR RBMS UNDER VARIOUS KINDS OF DYNAMICS |
d19340026 | Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models. | WHITENING BLACK-BOX NEURAL NETWORKS |
d35673326 | Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks. However, the theoretical principles for designing and training ResNets are still not fully understood. Recently, several points of view have emerged to try to interpret ResNet theoretically, such as unraveled view, unrolled iterative estimation and dynamical systems view. In this paper, we adopt the dynamical systems point of view, and analyze the lesioning properties of ResNet both theoretically and experimentally. Based on these analyses, we additionally propose a novel method for accelerating ResNet training. We apply the proposed method to train ResNets and Wide ResNets for three image classification benchmarks, reducing training time by more than 40% with superior or on-par accuracy. * Authors contributed equally. | MULTI-LEVEL RESIDUAL NETWORKS FROM DYNAMI- CAL SYSTEMS VIEW |
d49654320 | We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns. Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training. We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines. We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs. Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies. | VARIANCE REDUCTION FOR REINFORCEMENT LEARN- ING IN INPUT-DRIVEN ENVIRONMENTS |
d57761150 | To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task. | REVEALING INTERPRETABLE OBJECT REPRESENTA- TIONS FROM HUMAN BEHAVIOR |
d255749430 | Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps.One approach to understanding emergence is to find continuous progress measures that underlie the seemingly discontinuous qualitative changes.We argue that progress measures can be found via mechanistic interpretability: reverseengineering learned behaviors into their individual components.As a case study, we investigate the recently-discovered phenomenon of "grokking" exhibited by small transformers trained on modular addition tasks.We fully reverse engineer the algorithm learned by these networks, which uses discrete Fourier transforms and trigonometric identities to convert addition to rotation about a circle.We confirm the algorithm by analyzing the activations and weights and by performing ablations in Fourier space.Based on this understanding, we define progress measures that allow us to study the dynamics of training and split training into three continuous phases: memorization, circuit formation, and cleanup.Our results show that grokking, rather than being a sudden shift, arises from the gradual amplification of structured mechanisms encoded in the weights, followed by the later removal of memorizing components. | |
d9542459 | We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.The re-training process effectively performs distance metric learning under the object persistency constraints, to modify the view-manifold of object representations.It reduces the effective distance between the representations of different views of the same object without compromising the distance between those of the views of different objects, resulting in the untangling of the view-manifolds between individual objects within the same category and across categories.This untangling enables the model to discriminate and recognize objects within the same category, independent of viewpoints.We found that this ability is not limited to the trained objects, but transfers to novel objects in both trained and untrained categories, as well as to a variety of completely novel artificial synthetic objects.This transfer in learning suggests the modification of distance metrics in viewmanifolds is more general and abstract, likely at the levels of parts, and independent of the specific objects or categories experienced during training.Interestingly, the resulting transformation of feature representation in the deep networks is found to significantly better match human perceptual similarity judgment than AlexNet, suggesting that object persistence potentially could be a important constraint in the development of perceptual similarity judgment in our brains. | TRANSFER OF VIEW-MANIFOLD LEARNING TO SIMI-LARITY PERCEPTION OF NOVEL OBJECTS |
d253708071 | To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model to aggregate the output of multiple LFs to produce training labels. Most existing label models require a parameter learning step for each dataset. In this work, we present a hyper label model that (once learned) infers the ground-truth labels for each dataset in a single forward pass without dataset-specific parameter learning. The hyper label model approximates an optimal analytical (yet computationally intractable) solution of the ground-truth labels. We train the model on synthetic data generated in the way that ensures the model approximates the analytical optimal solution, and build the model upon Graph Neural Network (GNN) to ensure the model prediction being invariant (or equivariant) to the permutation of LFs (or data points). On 14 real-world datasets, our hyper label model outperforms the best existing methods in both accuracy (by 1.4 points on average) and efficiency (by six times on average). Our code is available at https://github.com/wurenzhi/hyper label model Published as a conference paper at ICLR 2023 two desiderata: (1) it works with "minimal" assumption, i.e., we only assume the majority of LFs is better-then-random while does not require the knowledge or assume any particular forms of underlying distribution p(y[i]|X[i, :]; θ); (2) once the hyper model is learned, it can be used to infer y for any new X without additional dataset-specific parameter learning process. To shed light on this direction, we first show, in theory, that without assuming underlying distribution, there is an optimal and analytical (therefore no parameter learning) way to estimate of y based on X, i.e., y * = h * (X). However, such h * is intractable to compute since it involves averaging over a set whose size is exponentially-increasing w.r.t.the size of X. Therefore, we propose to leverage the power of deep learning to approximate this solution, i.e., we seek for an alternative function h parametrized by some neural networks, and once learned, it can estimate the label vector for new dataset without ad hoc dataset-specific learning process. Thus, we call the learned model hyper label model.Materializing this idea involves two key questions: (1) How to generate training data? (2) How to design the model architecture? To generate training data, the straightforward solution is to use the analytical method to generate many pairs of (X, y * ) where y * = h * (X). However, computing y * with h * (X) is of exponential complexity. We notice that for each X, h * (X) is an average of the label vectors from a certain set. Taking advantage of this, we are able to avoid directly generating y * that is of exponential complexity and design a way of generating an equivalent set of training data such that the trained model approximates h * (X). | Published as a conference paper at ICLR 2023 LEARNING HYPER LABEL MODEL FOR PROGRAMMATIC WEAK SUPERVISION |
d257039062 | We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of 'semantic equivalence'-different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy-an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to 'off-the-shelf' language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines. | Published as a conference paper at ICLR 2023 SEMANTIC UNCERTAINTY: LINGUISTIC INVARIANCES FOR UNCERTAINTY ESTIMATION IN NATURAL LANGUAGE GENERATION |
d231985673 | Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization of model parameters (that we call meta-model) to rapidly adapt to new tasks using a small amount of labeled training data. Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning. In addition to generalization, robustness is also desired for a meta-model to defend adversarial examples (attacks). Toward promoting adversarial robustness in MAML, we first study when a robustness-promoting regularization should be incorporated, given the fact that MAML adopts a bi-level (fine-tuning vs. meta-update) learning procedure. We show that robustifying the meta-update stage is sufficient to make robustness adapted to the task-specific fine-tuning stage even if the latter uses a standard training protocol. We also make additional justification on the acquired robustness adaptation by peering into the interpretability of neurons' activation maps. Furthermore, we investigate how robust regularization can efficiently be designed in MAML. We propose a general but easily-optimized robustness-regularized meta-learning framework, which allows the use of unlabeled data augmentation, fast adversarial attack generation, and computationally-light fine-tuning. In particular, we for the first time show that the auxiliary contrastive learning task can enhance the adversarial robustness of MAML. Finally, extensive experiments are conducted to demonstrate the effectiveness of our proposed methods in robust few-shot learning. Codes are available at https://github.com/wangren09/MetaAdv. | Published as a conference paper at ICLR 2021 ON FAST ADVERSARIAL ROBUSTNESS ADAPTATION IN MODEL-AGNOSTIC META-LEARNING |
d231639408 | Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework. However, only a few works have explored unsupervised disentangled sequential representation learning due to challenges of generating sequential data. In this paper, we propose recurrent Wasserstein Autoencoder (R-WAE), a new framework for generative modeling of sequential data. R-WAE disentangles the representation of an input sequence into static and dynamic factors (i.e., time-invariant and time-varying parts). Our theoretical analysis shows that, R-WAE minimizes an upper bound of a penalized form of the Wasserstein distance between model distribution and sequential data distribution, and simultaneously maximizes the mutual information between input data and different disentangled latent factors, respectively. This is superior to (recurrent) VAE which does not explicitly enforce mutual information maximization between input data and disentangled latent representations. When the number of actions in sequential data is available as weak supervision information, R-WAE is extended to learn a categorical latent representation of actions to improve its disentanglement. Experiments on a variety of datasets show that our models outperform other baselines with the same settings in terms of disentanglement and unconditional video generation both quantitatively and qualitatively. | Published as a conference paper at ICLR 2021 DISENTANGLED RECURRENT WASSERSTEIN AUTOEN- CODER |
d252668296 | Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic certified robustness against geometric transformations into the training procedure, as existing verifiers are exceedingly slow. To address these challenges, we propose the first provable defense for deterministic certified geometric robustness. Our framework leverages a novel GPU-optimized verifier that can certify images between 60× to 42,600× faster than existing geometric robustness verifiers, and thus unlike existing works, is fast enough for use in training. Across multiple datasets, our results show that networks trained via our framework consistently achieve state-of-the-art deterministic certified geometric robustness and clean accuracy. Furthermore, for the first time, we verify the geometric robustness of a neural network for the challenging, real-world setting of autonomous driving.arXiv:2207.11177v3 [cs.LG] 6 May 2023Published as a conference paper at ICLR 2023 verification too expensive for use during training. Hence, training DNNs for deterministic certified robustness against geometric perturbations requires not only formulating the construction of a provable defense, but also completely redesigning geometric robustness verifiers for scalability. This Work. To address the outlined challenges, we propose Certified Geometric Training (CGT), a framework for training neural networks that are deterministically certified robust to geometric transformations. The framework consists of (1) the Fast Geometric Verifier (FGV), a novel method to perform geometric robustness certification that is orders of magnitude faster than the state-of-theart and (2) computationally efficient loss functions that embed FGV into the training procedure.We empirically evaluate our method on the MNIST (LeCun et al., 1998), CIFAR10 (Krizhevsky, 2009), Tiny ImageNet (Le & Yang, 2015, and Udacity self-driving car (Udacity, 2016) datasets to demonstrate CGT's effectiveness. Our results show that CGT-trained networks consistently achieve state-of-the-art clean accuracy and certified robustness; furthermore, FGV is between 60× to 42,600× faster than the state-of-the-art verifier for certifying each image. We also achieve several breakthroughs: (1) FGV enables us to certify deterministic robustness against geometric transformations on entire test sets of 10,000 images, which is more than 50× the number of images over existing works(100 in Balunovic et al. (2019) and 200 in Mohapatra et al. (2020)); (2) we are the first to scale deterministic geometric verification beyond CIFAR10; and(3)we are the first to verify a neural network for autonomous driving under realistic geometric perturbations. Our code is publicly available at https://github.com/uiuc-arc/CGT. | Published as a conference paper at ICLR 2023 PROVABLE DEFENSE AGAINST GEOMETRIC TRANSFORMATIONS |
d244714159 | We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs). Starting from a recently proposed Fourier representation of flow fields, the F-FNO bridges the performance gap between pure machine learning approaches to that of the best numerical or hybrid solvers. This is achieved with new representations -separable spectral layers and improved residual connections -and a combination of training strategies such as the Markov assumption, Gaussian noise, and cosine learning rate decay. On several challenging benchmark PDEs on regular grids, structured meshes, and point clouds, the F-FNO can scale to deeper networks and outperform both the FNO and the geo-FNO, reducing the error by 83% on the Navier-Stokes problem, 31% on the elasticity problem, 57% on the airfoil flow problem, and 60% on the plastic forging problem. Compared to the state-of-the-art pseudo-spectral method, the F-FNO can take a step size that is an order of magnitude larger in time and achieve an order of magnitude speedup to produce the same solution quality.Published as a conference paper at ICLR 2023 Overall, we make the following three key contributions:1. We propose a new representation, the F-FNO, which consists of separable Fourier representation and improved residual connections, reducing the model complexity and allowing it to scale to deeper networks (Fig. 2and Eqs.(7)and(8)).2. We show the importance of incorporating training techniques from the existing literature, such as Markov assumption, Gaussian noise, and cosine learning rate decay(Fig. 3); and investigate how well the operator can handle different input representations(Fig. 5).3. We demonstrate F-FNO's strong performance in a variety of geometries and PDEs (Fig. 3andTable 2). Code, datasets, and pre-trained models are available 1 . | Published as a conference paper at ICLR 2023 FACTORIZED FOURIER NEURAL OPERATORS |
d211132423 | Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate bias. In this paper, we 1) highlight that the effect of overestimation bias on learning efficiency is environment-dependent; 2) propose a generalization of Qlearning, called Maxmin Q-learning, which provides a parameter to flexibly control bias; 3) show theoretically that there exists a parameter choice for Maxmin Q-learning that leads to unbiased estimation with a lower approximation variance than Q-learning; and 4) prove the convergence of our algorithm in the tabular case, as well as convergence of several previous Q-learning variants, using a novel Generalized Q-learning framework. We empirically verify that our algorithm better controls estimation bias in toy environments, and that it achieves superior performance on several benchmark problems. 1 | MAXMIN Q-LEARNING: CONTROLLING THE ESTIMA- TION BIAS OF Q-LEARNING |
d51758422 | The goal of two-sample tests is to assess whether two samples, S P ∼ P n and S Q ∼ Q m , are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the n examples in S P with a positive label, and by pairing the m examples in S Q with a negative label. If the null hypothesis "P = Q" is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where P and Q differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery. | Published as a conference paper at ICLR 2017 REVISITING CLASSIFIER TWO-SAMPLE TESTS |
d252668882 | Human visual perception can easily generalize to out-of-distributed visual data, which is far beyond the capability of modern machine learning models. Domain generalization (DG) aims to close this gap, with existing DG methods mainly focusing on the loss function design. In this paper, we propose to explore an orthogonal direction, i.e., the design of the backbone architecture. It is motivated by an empirical finding that transformer-based models trained with empirical risk minimization (ERM) outperform CNN-based models employing state-ofthe-art (SOTA) DG algorithms on multiple DG datasets. We develop a formal framework to characterize a network's robustness to distribution shifts by studying its architecture's alignment with the correlations in the dataset. This analysis guides us to propose a novel DG model built upon vision transformers, namely Generalizable Mixture-of-Experts (GMoE). Extensive experiments on DomainBed demonstrate that GMoE trained with ERM outperforms SOTA DG baselines by a large margin. Moreover, GMoE is complementary to existing DG methods and its performance is substantially improved when trained with DG algorithms.Published as a conference paper at ICLR 2023 CNN architectures have different performances on DG datasets. Inspired by these pioneering works, we conjecture that backbone architecture design would be promising for DG. To verify this intuition, we evaluate a transformer-based model and compare it with CNN-based architectures of equivalent computational overhead, as shown inFig. 1(a). To our surprise, a vanilla ViT-S/16 (Dosovitskiy et al., 2021) trained with empirical risk minimization (ERM) outperforms ResNet-50 trained with SOTA DG algorithms(Cha et al., 2021b;Rame et al., 2021;Shi et al., 2021) on DomainNet, OfficeHome and VLCS datasets, despite the fact that both architectures have a similar number of parameters and enjoy close performance on in-distribution domains. We theoretically validate this effect based on the algorithmic alignment framework (Xu et al., 2020a;Li et al., 2021). We first prove that a network trained with the ERM loss function is more robust to distribution shifts if its architecture is more similar to the invariant correlation, where the similarity is formally measured by the alignment value defined in Xu et al. (2020a). On the contrary, a network is less robust if its architecture aligns with the spurious correlation. We then investigate the alignment between backbone architectures (i.e., convolutions and attentions) and the correlations in these datasets, which explains the superior performance of ViT-based methods.To further improve the performance, our analysis indicates that we should exploit properties of invariant correlations in vision tasks and design network architectures to align with these properties. This requires an investigation that sits at the intersection of domain generalization and classic computer vision. In domain generalization, it is widely believed that the data are composed of some sets of attributes and distribution shifts of data are distribution shifts of these attributes (Wiles et al., 2021). The latent factorization model of these attributes is almost identical to the generative model of visual attributes in classic computer vision (Ferrari & Zisserman, 2007). To capture these diverse attributes, we propose a Generalizable Mixture-of-Experts (GMoE), which is built upon sparse mixture-of-experts (sparse MoEs) and vision transformer (Dosovitskiy et al., 2021). The sparse MoEs were originally proposed as key enablers for extremely large, but efficient models (Fedus et al., 2022). By theoretical and empirical evidence, we demonstrate that MoEs are experts for processing visual attributes, leading to a better alignment with invariant correlations. Based on our analysis, we modify the architecture of sparse MoEs to enhance their performance in DG. Extensive experiments demonstrate that GMoE achieves superior domain generalization performance both with and without DG algorithms.CONTRIBUTIONSIn this paper, we formally investigate the impact of the backbone architecture on DG and propose to develop effective DG methods by backbone architecture design. Specifically, our main contributions are summarized as follows:A Novel View of DG: In contrast to previous works, this paper initiates a formal exploration of the backbone architecture in DG. Based on algorithmic alignment (Xu et al., 2020a), we prove that a network is more robust to distribution shifts if its architecture aligns with the invariant correlation, whereas less robust if its architecture aligns with spurious correlation. The theorems are verified on synthetic and real datasets.A Novel Model for DG: Based on our theoretical analysis, we propose Generalizable Mixture-of-Experts (GMoE) and prove that it enjoys a better alignment than vision transformers. GMoE is built upon sparse mixture-of-experts (Shazeer et al., 2017) and vision transformer (Dosovitskiy et al., 2021), with a theory-guided performance enhancement for DG.Excellent Performance: We validate GMoE's performance on all 8 large-scale datasets of Do-mainBed. Remarkably, GMoE trained with ERM achieves SOTA performance on 7 datasets in the train-validation setting and on 8 datasets in the leave-one-domain-out setting. Furthermore, the GMoE trained with DG algorithms achieves better performance than GMoE trained with ERM.PRELIMINARIES2.1 NOTATIONS Throughout this paper, a, a, A stand for a scalar, a column vector, a matrix, respectively. O(·) and ω(·) are asymptotic notations. We denote the training dataset, training distribution, test dataset, and test distribution as E tr , D tr , E te , and D te , respectively. | SPARSE MIXTURE-OF-EXPERTS ARE DOMAIN GENER- ALIZABLE LEARNERS |
d6667083 | Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the input to seem substantially different to human perception. We deploy three methods to detect adversarial images. Adversaries trying to bypass our detectors must make the adversarial image less pathological or they will fail trying. Our best detection method reveals that adversarial images place abnormal emphasis on the lower-ranked principal components from PCA. Other detectors and a colorful saliency map are in an appendix. * Work done while the author was at TTIC. Code available at github.com/hendrycks/fooling Takaya Saito and Marc Rehmsmeier. The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. In PLoS ONE. 2015.Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image | Workshop track -ICLR 2017 EARLY METHODS FOR DETECTING ADVERSARIAL IMAGES |
d898000 | Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a 'wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels (54 3 ) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks. | Deep and Wide Multiscale Recursive Networks for Robust Image Labeling |
d253158053 | Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly initialized with random weights. These attract our interest in conducting a systematic study to explore pre-training for FL. Across multiple visual recognition benchmarks, we found that pre-training can not only improve FL, but also close its accuracy gap to the counterpart centralized learning, especially in the challenging cases of non-IID clients' data. To make our findings applicable to situations where pre-trained models are not directly available, we explore pre-training with synthetic data or even with clients' data in a decentralized manner, and found that they can already improve FL notably. Interestingly, many of the techniques we explore are complementary to each other to further boost the performance, and we view this as a critical result toward scaling up deep FL for real-world applications. We conclude our paper with an attempt to understand the effect of pre-training on FL. We found that pre-training enables the learned global models under different clients' data conditions to converge to the same loss basin, and makes global aggregation in FL more stable. Nevertheless, pre-training seems to not alleviate local model drifting, a fundamental problem in FL under non-IID data. self-supervised models are strong semi-supervised learners. In NeurIPS, 2020c.Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, pp.15750-15758, 2021.Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020d. : Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. , et al. Self-supervised pretraining of visual features in the wild. arXiv preprint arXiv:2103.01988, 2021. | Published as a conference paper at ICLR 2023 ON THE IMPORTANCE AND APPLICABILITY OF PRE-TRAINING FOR FEDERATED LEARNING |
d258564695 | Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error on the weights. Recent work assumes the approximation error on the weights is a proxy for the performance of the model to compress multiple layers and fine-tune the compressed model. Surprisingly, little research has systematically evaluated which approximation errors can be used to make choices regarding the layer, tensor decomposition method, and level of compression. To close this gap, we perform an experimental study to test if this assumption holds across different layers and types of decompositions, and what the effect of fine-tuning is. We include the approximation error on the features resulting from a compressed layer in our analysis to test if this provides a better proxy, as it explicitly takes the data into account. We find the approximation error on the weights has a positive correlation with the performance error, before as well as after fine-tuning. Basing the approximation error on the features does not improve the correlation significantly. While scaling the approximation error commonly is used to account for the different sizes of layers, the average correlation across layers is smaller than across all choices (i.e. layers, decompositions, and level of compression) before fine-tuning. When calculating the correlation across the different decompositions, the average rank correlation is larger than across all choices. This means multiple decompositions can be considered for compression and the approximation error can be used to choose between them.arXiv:2305.05318v2 [cs.LG] 4 Aug 2023Published as a conference paper at ICLR 2023While this assumption appears intuitive and reasonable, we observe several gaps in the existing literature: First, most existing TD compression literature only focuses on a few decomposition choices, e.g. fixing the TD method(Lebedev et al., 2015;Kim et al., 2016). Although various error measures and decomposition choices have been studied in separation, no prior work systematically compares different decomposition errors across multiple decomposition choices. Second, different decomposition errors with different properties have been used throughout the literature(Jaderberg et al., 2014), and it is unclear if some error measure should be preferred. Third, a benefit of TD is that no training data is needed for compression, though if labeled data is available, more recent methods combine TD with a subsequent fine-tuning step. Is the approximation error equally valid for the model performance with and without fine-tuning?Overall, to the best of the authors' knowledge, no prior work investigates if and which decomposition choices for TD network compression can be made using specific approximation errors. This paper studies empirically to what extent a single decomposition error correlates with the compressed model's performance across varied decomposition choices, identifying how existing procedures could be improved, and providing support for specific practices. Our contributions are as follows: | Published as a conference paper at ICLR 2023 HOW INFORMATIVE IS THE APPROXIMATION ERROR FROM TENSOR DECOMPOSITION FOR NEURAL NET- WORK COMPRESSION? |
d251929437 | Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pretrained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods. Our implementation is available at https://github.com/ML-GSAI/Reg-ADA-APA.Published as a conference paper at ICLR 2023 this perspective, we propose a complementary framework, named regularized deep generative model (Reg-DGM), which employs a pre-trained model as regularization to achieve a better bias-variance tradeoff when training a DGM with limited data. : Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016. . Learning implicit generative models by teaching explicit ones. arXiv preprint arXiv:1807.03870, 2018.Kuzman Ganchev, Joao Graça, Jennifer Gillenwater, and Ben Taskar. Posterior regularization for structured latent variable models. . Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017.Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4401-4410, 2019. | Published as a conference paper at ICLR 2023 DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION BY NONTRANSFERABLE PRE-TRAINED MODELS |
d248178090 | Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue by leveraging differentiable simulators. However, inherent problems such as local minima and exploding/vanishing numerical gradients prevent these methods from being generally applied to control tasks with complex contact-rich dynamics, such as humanoid locomotion in classical RL benchmarks. In this work we present a high-performance differentiable simulator and a new policy learning algorithm (SHAC) that can effectively leverage simulation gradients, even in the presence of non-smoothness. Our learning algorithm alleviates problems with local minima through a smooth critic function, avoids vanishing/exploding gradients through a truncated learning window, and allows many physical environments to be run in parallel. We evaluate our method on classical RL control tasks, and show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms. In addition, we demonstrate the scalability of our method by applying it to the challenging high-dimensional problem of muscle-actuated locomotion with a large action space, achieving a greater than 17× reduction in training time over the best-performing established RL algorithm. More visual results are provided at: https://short-horizon-actor-critic.github.io/. | Published as a conference paper at ICLR 2022 ACCELERATED POLICY LEARNING WITH PARALLEL DIFFERENTIABLE SIMULATION |
d252683876 | This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for two notions of generalization errors. The first notion measures the gap between the population risk in the target domain and that in the source domain, and the second measures the gap between the population risk in the target domain and the empirical risk in the source domain. While our bounds for the first kind of error are in line with the traditional analysis and give similar insights, our bounds on the second kind of error are algorithm-dependent, which also provide insights into algorithm designs. Specifically, we present two simple techniques for improving generalization in UDA and validate them experimentally. | INFORMATION-THEORETIC ANALYSIS OF UNSUPER- VISED DOMAIN ADAPTATION |
d256389639 | We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a "diagonally grouped linear neural network". We show the following intriguing property of our reparameterization: gradient descent over the squared regression loss, without any explicit regularization, biases towards solutions with a group sparsity structure. In contrast to many existing works in understanding implicit regularization, we prove that our training trajectory cannot be simulated by mirror descent. We analyze the gradient dynamics of the corresponding regression problem in the general noise setting and obtain minimax-optimal error rates. Compared to existing bounds for implicit sparse regularization using diagonal linear networks, our analysis with the new reparameterization shows improved sample complexity. In the degenerate case of size-one groups, our approach gives rise to a new algorithm for sparse linear regression. Finally, we demonstrate the efficacy of our approach with several numerical experiments 1 .1 Code is available on https://github.com/jiangyuan2li/Implicit-Group-Sparsity arXiv:2301.12540v1 [stat.ML] 29 Jan 2023Outside of implicit regularization, several other works study the inductive bias of network architectures under explicit 2 regularization on model weights (Pilanci & Ergen, 2020; Sahiner et al., 2020). For multichannel linear convolutional networks, Jagadeesan et al.(2021)show that 2 -norm minimization of weights leads to a norm regularizer on predictors, where the norm is given by a semidefinite program (SDP). The representation cost in predictor space induced by explicit 2 regularization on (various different versions of) linear neural networks is studied in Dai et al. (2021), which demonstrates several interesting (induced) regularizers on the linear predictors such as p quasi-norms and group quasi-norms. However, these results are silent on the behavior of gradient descent-based training without explicit regularization. In light of the above results, we ask the following question:Beyond 2 -norm, sparsity and low-rankness, can gradient descent induce other forms of implicit regularization?Our contributions. In this paper, we rigorously show that a diagonally-grouped linear neural network (seeFigure 1b) trained by gradient descent with (proper/partial) weight normalization induces group-sparse regularization: a form of structured regularization that, to the best of our knowledge, has not been provably established in previous work.One major approach to understanding implicit regularization of gradient descent is based on its equivalence to a mirror descent (on a different objective function) (e.g., Gunasekar et al., 2018a; Woodworth et al., 2020). However, we show that, for the diagonally-grouped linear network architecture, the gradient dynamics is beyond mirror descent. We then analyze the convergence of gradient flow with early stopping under orthogonal design with possibly noisy observations, and show that the obtained solution exhibits an implicit regularization effect towards structured (specifically, group) sparsity. In addition, we show that weight normalization can deal with instability related to the choices of learning rates and initialization. With weight normalization, we are able to obtain a similar implicit regularization result but in more general settings: orthogonal/non-orthogonal designs with possibly noisy observations. Also, the obtained solution can achieve minimax-optimal error rates.Overall, compared to existing analysis of diagonal linear networks, our model design -that induces structured sparsity -exhibits provably improved sample complexity. In the degenerate case of size-one groups, our bounds coincide with previous results, and our approach can be interpreted as a new algorithm for sparse linear regression.Our techniques. Our approach is built upon the power reparameterization trick, which has been shown to promote model sparsity (Schwarz et al., 2021). Raising the parameters of a linear model element-wisely to the N -th power (N > 1) results in that parameters of smaller magnitude receive smaller gradient updates, while parameters of larger magnitude receive larger updates. In essence, this leads to a "rich get richer" phenomenon in gradient-based training. In Gissin et al. (2019) and Berthier (2022), the authors analyze the gradient dynamics on a toy example, and call this "incremental learning". Concretely, for a linear predictor w ∈ R p , if we re-parameterize the model as w = u •N − v •N (where u •N means the N -th element-wise power of u), then gradient descent will bias the training towards sparse solutions. This reparameterization is equivalent to a diagonal linear network, as shown inFigure 1a. This is further studied in Woodworth et al. (2020) for interpolating predictors, where they show that a small enough initialization induces 1 -norm regularization. For noisy settings, Vaskevicius et al.(2019)and Li et al. (2021) show that gradient descent converges to sparse models with early stopping. In the special case of sparse recovery from under-sampled Rong Ge, Jason D Lee, and Tengyu Ma. Learning one-hidden-layer neural networks with landscape design. arXiv preprint arXiv:1711.00501, 2017.Daniel Gissin, Shai Shalev-Shwartz, and Amit Daniely. The implicit bias of depth: How incremental learning drives generalization. arXiv preprint arXiv: | Published as a conference paper at ICLR 2023 IMPLICIT REGULARIZATION FOR GROUP SPARSITY |
d204749519 | Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and setting the firing threshold for each layer as the maximum input received in that layer. These type of converted SNNs require a large number of time steps to achieve competitive accuracy which diminishes the energy savings. The number of time steps can be reduced by training SNNs with spike-based backpropagation from scratch, but that is computationally expensive and slow. To address these challenges, we present a computationally-efficient training technique for deep SNNs 1 . We propose a hybrid training methodology: 1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time steps for input processing. STDB is performed with a novel surrogate gradient function defined using neuron's spike time. The weight update is proportional to the difference in spike timing between the current time step and the most recent time step the neuron generated an output spike. The SNNs trained with our hybrid conversion-and-STDB training perform at 10×−25× fewer number of time steps and achieve similar accuracy compared to purely converted SNNs. The proposed training methodology converges in less than 20 epochs of spike-based backpropagation for most standard image classification datasets, thereby greatly reducing the training complexity compared to training SNNs from scratch. We perform experiments on CIFAR-10, CIFAR-100 and ImageNet datasets for both VGG and ResNet architectures. We achieve top-1 accuracy of 65.19% for ImageNet dataset on SNN with 250 time steps, which is 10× faster compared to converted SNNs with similar accuracy. | Published as a conference paper at ICLR 2020 ENABLING DEEP SPIKING NEURAL NETWORKS WITH HYBRID CONVERSION AND SPIKE TIMING DEPENDENT BACKPROPAGATION |
d253735268 | Quantifying similarity between neural representations-e.g. hidden layer activation vectors-is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these measures of deterministic representational similarity ignore the scale and geometric structure of noise, both of which play important roles in neural computation. To rectify this, we generalize previously proposed shape metrics (Williams et al., 2021) to quantify differences in stochastic representations. These new distances satisfy the triangle inequality, and thus can be used as a rigorous basis for many supervised and unsupervised analyses. Leveraging this novel framework, we find that the stochastic geometries of neurobiological representations of oriented visual gratings and naturalistic scenes respectively resemble untrained and trained deep network representations. Further, we are able to more accurately predict certain network attributes (e.g. training hyperparameters) from its position in stochastic (versus deterministic) shape space. * Equal contribution. Published as a conference paper at ICLR 2023. | Published as a conference paper at ICLR 2023 REPRESENTATIONAL DISSIMILARITY METRIC SPACES FOR STOCHASTIC NEURAL NETWORKS |
d246441782 | Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information form solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS). Of particular interest are sparse attacks. The realisation of sparse attacks in blackbox models demonstrates that machine learning models are more vulnerable than we believe. Because, these attacks aim to minimize the number of perturbed pixels-measured by l 0 norm-required to mislead a model by solely observing the decision (the predicted label) returned to a model query; the so-called decisionbased attack setting. But, such an attack leads to an NP-hard optimization problem. We develop an evolution-based algorithm-SparseEvo-for the problem and evaluate against both convolutional deep neural networks and vision transformers. Notably, vision transformers are yet to be investigated under a decision-based attack setting. SparseEvo requires significantly fewer model queries than the stateof-the-art sparse attack Pointwise for both untargeted and targeted attacks. The attack algorithm, although conceptually simple, is also competitive with only a limited query budget against the state-of-the-art gradient-based whitebox attacks in standard computer vision tasks such as ImageNet. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general, raise new questions regarding the safety of deployed systems and poses new directions to study and understand the robustness of machine learning models. | Published as a conference paper at ICLR 2022 QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS |
d238408300 | We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one. Our method is based on the maximum bipartite matching algorithm and utilizes the Fisher Information matrix. We provide theoretical analyses demonstrating that the proposed score is mathematically well-defined, and subsequently use the affinity score to propose a novel algorithm for the few-shot learning problem. In particular, using this score, we find relevant training data labels to the test data and leverage the discovered relevant data for episodically fine-tuning a few-shot model. Results on various few-shot benchmark datasets demonstrate the efficacy of the proposed approach by improving the classification accuracy over the state-of-the-art methods even when using smaller models. | TASK AFFINITY WITH MAXIMUM BIPARTITE MATCH- ING IN FEW-SHOT LEARNING |
d17996690 | This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures. | PERMUTOHEDRAL LATTICE CNNS |
d219558695 | Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or un-oriented meshes is still a challenging task that feeds many downstream computer vision and graphics applications. In this paper we introduce SAL + + : a method for learning implicit neural representations of shapes directly from such raw data. We build upon the recent sign agnostic learning (SAL) approach and generalize it to include derivative data in a sign agnostic manner. In more detail, given the unsigned distance function to the input raw data, we suggest a novel sign agnostic regression loss, incorporating both pointwise values and gradients of the unsigned distance function. Optimizing this loss leads to a signed implicit function solution, the zero level set of which is a high quality, valid manifold approximation to the input 3D data. We demonstrate the efficacy of SAL + + by shape space learning from two challenging datasets: ShapeNet [9] that contains inconsistent orientation and non-manifold meshes, and D-Faust [8] that contains raw 3D scans (triangle soups). On both these datasets we present state of the art results. | SAL ++ : Sign Agnostic Learning with Derivatives |
d249626454 | Knowledge distillation (KD) has shown very promising capabilities in transferring learning representations from large models (teachers) to small models (students). However, as the capacity gap between students and teachers becomes larger, existing KD methods fail to achieve better results. Our work shows that the 'prior knowledge' is vital to KD, especially when applying large teachers. Particularly, we propose the dynamic prior knowledge (DPK), which integrates part of teacher's features as the prior knowledge before the feature distillation. This means that our method also takes the teacher's feature as 'input', not just 'target'. Besides, we dynamically adjust the ratio of the prior knowledge during the training phase according to the feature gap, thus guiding the student in an appropriate difficulty. To evaluate the proposed method, we conduct extensive experiments on two image classification benchmarks (i.e. CIFAR100 and ImageNet) and an object detection benchmark (i.e. MS COCO). The results demonstrate the superiority of our method in performance under varying settings. Besides, our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers. More importantly, DPK provides a fast solution in teacher model selection for any given model.Published as a conference paper at ICLR 2023 hard to 'understand' the high-order semantics extracted by the large model. This problem will be exacerbated when applying larger teachers, and it makes the student's accuracy inversely correlated with the capacity of the teacher model 1 . Note that this problem also exists for humans, and human teachers often tell students some prior knowledge to facilitates their learning in this case. Moreover, the experienced teachers can also adjust the amounts of provided prior knowledge accordingly for different students to fully develop their potentials. quantization block for cnns. In ECCV, 2020. Show, attend and distill: Knowledge distillation via attentionbased feature matching. In AAAI, 2021. | Published as a conference paper at ICLR 2023 BETTER TEACHER BETTER STUDENT: DYNAMIC PRIOR KNOWLEDGE FOR KNOWLEDGE DISTILLATION |
d257378597 | Rich data and powerful machine learning models allow us to design drugs for a specific protein target in silico. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free models as the atomic interaction in the 3D space is explicitly modeled. However, current 3D target-aware models either rely on the voxelized atom densities or the autoregressive sampling process, which are not equivariant to rotation or easily violate geometric constraints resulting in unrealistic structures. In this work, we develop a 3D equivariant diffusion model to solve the above challenges. To achieve target-aware molecule design, our method learns a joint generative process of both continuous atom coordinates and categorical atom types with a SE(3)-equivariant network. Moreover, we show that our model can serve as an unsupervised feature extractor to estimate the binding affinity under proper parameterization, which provides an effective way for drug screening. To evaluate our model, we propose a comprehensive framework to evaluate the quality of sampled molecules from different dimensions. Empirical studies show our model could generate molecules with more realistic 3D structures and better affinities towards the protein targets, and improve binding affinity ranking and prediction without retraining. | Published as a conference paper at ICLR 2023 3D EQUIVARIANT DIFFUSION FOR TARGET-AWARE MOLECULE GENERATION AND AFFINITY PREDICTION |
d247058767 | Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problems by penalizing the Q-values of OOD actions or constraining the trained policy to be close to the behavior policy. Nevertheless, such methods typically prevent the generalization of value functions beyond the offline data and also lack a precise characterization of OOD data. In this paper, we propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints. Specifically, PBRL conducts uncertainty quantification via the disagreement of bootstrapped Q-functions, and performs pessimistic updates by penalizing the value function based on the estimated uncertainty. To tackle the extrapolating error, we further propose a novel OOD sampling method. We show that such OOD sampling and pessimistic bootstrapping yields a provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL. Extensive experiments on D4RL benchmark show that PBRL has better performance compared to the state-of-the-art algorithms. Song. Uncertainty-based offline reinforcement learning with diversified q-ensemble. In Advances in neural information processing systems, 2021.Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos. Minimax regret bounds for reinforcement learning. | PESSIMISTIC BOOTSTRAPPING FOR UNCERTAINTY- DRIVEN OFFLINE REINFORCEMENT LEARNING |
d247958259 | Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual learning (SCL) scenarios. Consequently, they are not scalable to real-world applications where the data distribution is often biased and unannotated. In this work, we focus on unsupervised continual learning (UCL), where we learn the feature representations on an unlabelled sequence of tasks and show that reliance on annotated data is not necessary for continual learning. We conduct a systematic study analyzing the learned feature representations and show that unsupervised visual representations are surprisingly more robust to catastrophic forgetting, consistently achieve better performance, and generalize better to out-ofdistribution tasks than SCL. Furthermore, we find that UCL achieves a smoother loss landscape through qualitative analysis of the learned representations and learns meaningful feature representations. Additionally, we propose Lifelong Unsupervised Mixup (LUMP), a simple yet effective technique that interpolates between the current task and previous tasks' instances to alleviate catastrophic forgetting for unsupervised representations. We release our code online. | Published as a conference paper at ICLR 2022 REPRESENTATIONAL CONTINUITY FOR UNSUPERVISED CONTINUAL LEARNING |
d211678036 | We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training L-hidden-layer linear residual networks (ResNets). We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD with zero initialization on all hidden weights can converge to the global minimum of the training loss. Moreover, when specializing to appropriate Gaussian random linear transformations, GD and SGD provably optimize wide enough deep linear ResNets. Compared with the global convergence result of GD for training standard deep linear networks (Du & Hu, 2019), our condition on the neural network width is sharper by a factor of OpκLq, where κ denotes the condition number of the covariance matrix of the training data. We further propose a modified identity input and output transformations, and show that a pd`kq-wide neural network is sufficient to guarantee the global convergence of GD/SGD, where d, k are the input and output dimensions respectively. | Published as a conference paper at ICLR 2020 ON THE GLOBAL CONVERGENCE OF TRAIN- ING DEEP LINEAR RESNETS |
d3882452 | We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space. Second, discriminators of GANs easily forget about past generated samples by generators, incurring instability during adversarial training. We argue that these two infamous problems of unsupervised GAN training can be largely alleviated by a learnable memory network to which both generators and discriminators can access. Generators can effectively learn representation of training samples to understand underlying cluster distributions of data, which ease the structure discontinuity problem. At the same time, discriminators can better memorize clusters of previously generated samples, which mitigate the forgetting problem. We propose a novel end-to-end GAN model named memoryGAN, which involves a memory network that is unsupervisedly trainable and integrable to many existing GAN models. With evaluations on multiple datasets such as Fashion-MNIST, CelebA, CIFAR10, and Chairs, we show that our model is probabilistically interpretable, and generates realistic image samples of high visual fidelity. The memoryGAN also achieves the state-of-the-art inception scores over unsupervised GAN models on the CIFAR10 dataset, without any optimization tricks and weaker divergences. | Published as a conference paper at ICLR 2018 MEMORIZATION PRECEDES GENERATION: LEARNING UNSUPERVISED GANS WITH MEMORY NETWORKS |
d2220097 | GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the feature-matching GAN of Salimans et al.(2016), we achieve state-of-the-art results for GAN-based semisupervised learning on the CIFAR-10 dataset, with a method that is significantly easier to implement than competing methods. * All code and hyperparameters may be found at https://github.com/bruno-31/ GAN-manifold-regularization arXiv:1805.08957v1 [cs.LG] | Workshop track -ICLR 2018 SEMI-SUPERVISED LEARNING WITH GANS: REVISITING MANIFOLD REGULARIZATION |
d4717445 | We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parameterised by the score matrices, must alone be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack. | Published as a conference paper at ICLR 2018 LEARN TO PAY ATTENTION |
d8241258 | The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed "Actor-Mimic", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers. We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments. Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods. | ACTOR-MIMIC DEEP MULTITASK AND TRANSFER REINFORCEMENT LEARNING |
d11345245 | In recent years, Deep Learning (DL) has found great success in domains such as multimedia understanding. However, the complex nature of multimedia data makes it difficult to develop DL-based software. The state-of-the-art tools, such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their applicable domains, are programming libraries with fixed user interface, internal representation, and execution environment. This makes it difficult to implement portable and customized DL applications. In this paper, we present DeepDSL, a domain specific language (DSL) embedded in Scala, that compiles deep networks written in DeepDSL to Java source code. Deep DSL provides (1) intuitive constructs to support compact encoding of deep networks; (2) symbolic gradient derivation of the networks; (3) static analysis for memory consumption and error detection; and (4) DSL-level optimization to improve memory and runtime efficiency. DeepDSL programs are compiled into compact, efficient, customizable, and portable Java source code, which operates the CUDA and CUDNN interfaces running on Nvidia GPU via a Java Native Interface (JNI) library. We evaluated DeepDSL with a number of popular DL networks. Our experiments show that the compiled programs have very competitive runtime performance and memory efficiency compared to the existing libraries. | DEEPDSL: A COMPILATION-BASED DOMAIN- SPECIFIC LANGUAGE FOR DEEP LEARNING |
d249097525 | Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a generalization error, that is, an expected value of a given loss function. However, for several applications -for example in a security-critical context or for problems in the computational sciences -accuracy in this sense is not sufficient. In such cases, one would like to have guarantees for high accuracy on every input value, that is, with respect to the uniform norm. In this paper we precisely quantify the number of training samples needed for any conceivable training algorithm to guarantee a given uniform accuracy on any learning problem formulated over target classes containing (or consisting of) ReLU neural networks of a prescribed architecture. We prove that, under very general assumptions, the minimal number of training samples for this task scales exponentially both in the depth and the input dimension of the network architecture. | Published as a conference paper at ICLR 2023 LEARNING RELU NETWORKS TO HIGH UNIFORM ACCURACY IS INTRACTABLE |
d254044293 | Graph neural networks (GNNs) are prominent in the graph machine learning domain, owing to their strong performance across various tasks. A recent focal area is the space of graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Notably, many state-of-theart graph SSL approaches are contrastive methods, which use a combination of positive and negative samples to learn node representations. Owing to challenges in negative sampling (slowness and model sensitivity), recent literature introduced non-contrastive methods, which instead only use positive samples. Though such methods have shown promising performance in node-level tasks, their suitability for link prediction tasks, which are concerned with predicting link existence between pairs of nodes, and have broad applicability to recommendation systems contexts, is yet unexplored. In this work, we extensively evaluate the performance of existing non-contrastive methods for link prediction in both transductive and inductive settings. While most existing non-contrastive methods perform poorly overall, we find that, surprisingly, BGRL generally performs well in transductive settings. However, it performs poorly in the more realistic inductive settings where the model has to generalize to links to/from unseen nodes. We find that non-contrastive models tend to overfit to the training graph and use this analysis to propose T-BGRL, a novel non-contrastive framework that incorporates cheap corruptions to improve the generalization ability of the model. This simple modification strongly improves inductive performance in 5/6 of our datasets, with up to a 120% improvement in Hits@50-all with comparable speed to other non-contrastive baselines, and up to 14× faster than the best-performing contrastive baseline. Our work imparts interesting findings about non-contrastive learning for link prediction and paves the way for future researchers to further expand upon this area. | Published as a conference paper at ICLR 2023 LINK PREDICTION WITH NON-CONTRASTIVE LEARNING |
d11189705 | In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo parent-class label to each observation which is then modified by applying the domain specific transformation associated with the assigned label. Generated pseudo observation-label pairs are subsequently used to train a neural network with Auto-clustering Output Layer (ACOL) that introduces multiple softmax nodes for each pseudo parent-class. Due to the unsupervised objective based on Graph-based Activity Regularization (GAR) terms, softmax duplicates of each parent-class are specialized as the hidden information captured through the help of domain specific transformations is propagated during training. Ultimately we obtain a k-means friendly latent representation. Furthermore, we demonstrate how the chosen transformation type impacts performance and helps propagate the latent information that is useful in revealing unknown clusters. Our results show state-of-the-art performance for unsupervised clustering tasks on MNIST, SVHN and USPS datasets, with the highest accuracies reported to date in the literature. | Published as a conference paper at ICLR 2018 LEARNING LATENT REPRESENTATIONS IN NEURAL NETWORKS FOR CLUSTERING THROUGH PSEUDO SUPERVISION AND GRAPH-BASED ACTIVITY REGULARIZATION |
d3280568 | While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks. In this paper, we study a simple reinforcement learning problem and focus on learning policies that encode the proper invariances for generalization to different settings. We evaluate three potential methods for policy generalization: data augmentation, meta-learning and adversarial training. We find our data augmentation method to be effective, and study the potential of metalearning and adversarial learning as alternative task-agnostic approaches. | Workshop track -ICLR 2018 LEARNING INVARIANCES FOR POLICY GENERALIZATION |
d219636258 | Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -the free algebra -to capture this non-commutativity. To address the innate computational complexity of this algebra, we use compositions of low-rank tensor projections. This yields modular and scalable building blocks that give state-of-the-art performance on standard benchmarks such as multivariate time series classification, mortality prediction and generative models for video. Code and benchmarks are publically available at https://github.com/tgcsaba/seq2tens. | Published as a conference paper at ICLR 2021 SEQ2TENS: AN EFFICIENT REPRESENTATION OF SE- QUENCES BY LOW-RANK TENSOR PROJECTIONS |
d215827885 | Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly understood. This paper investigates how different normalization techniques affect the performance of neural ODEs. Particularly, we show that it is possible to achieve 93% accuracy in the CIFAR-10 classification task, and to the best of our knowledge, this is the highest reported accuracy among neural ODEs tested on this problem. | TOWARDS UNDERSTANDING NORMALIZATION IN NEURAL ODES |
d252762165 | As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTRANS which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTRANS on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTRANS performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings. Code is released at https://github.com | Published as a conference paper at ICLR 2023 EMPOWERING GRAPH REPRESENTATION LEARNING WITH TEST-TIME GRAPH TRANSFORMATION |
d208637067 | In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at Published as a conference paper at ICLR 2020 cars will likely need to understand the point of view, intents and beliefs of other traffic participants in order to deal with highly interactive settings such as 4-way crossing or dense traffic in cities.Hanabi is a fully cooperative, partially-observable card game that has recently been proposed as a new benchmark challenge problem for AI research(Bard et al., 2019)to fill the gap around ToM. In Hanabi, players need to find conventions that allow them to effectively exchange information from their local observations through their actions, taking advantage of the fact that actions are observed by all team mates.Most prior state-of-the-art agents for Hanabi were developed using handcrafted algorithms, which beat off-the-shelf deep multi-agent RL methods by a large margin. This makes intuitive sense: Beyond the "standard" multi-agent challenges of credit assignment, nonstationarity and joint exploration, learning an informative policy presents an additional fundamentally new conflict. On the one hand, an RL agent needs to explore in order to discover good policies through trial and error. On the other hand, when carried out naively, this exploration will add noise to the policy of the agent during the training process, making their actions strictly less informative to their team mates. | Published as a conference paper at ICLR 2020 SIMPLIFIED ACTION DECODER FOR DEEP MULTI-AGENT REINFORCEMENT LEARNING |
d252715543 | It is unclear how changing the learning rule of a deep neural network alters its learning dynamics and representations. To gain insight into the relationship between learned features, function approximation, and the learning rule, we analyze infinite-width deep networks trained with gradient descent (GD) and biologicallyplausible alternatives including feedback alignment (FA), direct feedback alignment (DFA), and error modulated Hebbian learning (Hebb), as well as gated linear networks (GLN). We show that, for each of these learning rules, the evolution of the output function at infinite width is governed by a time varying effective neural tangent kernel (eNTK). In the lazy training limit, this eNTK is static and does not evolve, while in the rich mean-field regime this kernel's evolution can be determined self-consistently with dynamical mean field theory (DMFT). This DMFT enables comparisons of the feature and prediction dynamics induced by each of these learning rules. In the lazy limit, we find that DFA and Hebb can only learn using the last layer features, while full FA can utilize earlier layers with a scale determined by the initial correlation between feedforward and feedback weight matrices. In the rich regime, DFA and FA utilize a temporally evolving and depthdependent NTK. Counterintuitively, we find that FA networks trained in the rich regime exhibit more feature learning if initialized with smaller correlation between the forward and backward pass weights. GLNs admit a very simple formula for their lazy limit kernel and preserve conditional Gaussianity of their preactivations under gating functions. Error modulated Hebb rules show very small task-relevant alignment of their kernels and perform most task relevant learning in the last layer. | THE INFLUENCE OF LEARNING RULE ON REPRESEN- TATION DYNAMICS IN WIDE NEURAL NETWORKS |
d8217340 | With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep architectures.Here we show that object detectors emerge from training CNNs to perform scene classification. As scenes are composed of objects, the CNN for scene classification automatically discovers meaningful objects detectors, representative of the learned scene categories. With object detectors emerging as a result of learning to recognize scenes, our work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having explicitly learned the notion of objects. | OBJECT DETECTORS EMERGE IN DEEP SCENE CNNS |
d256697430 | Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approximate steps of classical algorithms. In this context, much of the current work has focused on learning reachability and shortest path graph algorithms, showing that joint learning on similar algorithms is beneficial for generalisation. However, when targeting more complex problems, such "similar" algorithms become more difficult to find. Here, we propose to learn algorithms by exploiting duality of the underlying algorithmic problem. Many algorithms solve optimisation problems. We demonstrate that simultaneously learning the dual definition of these optimisation problems in algorithmic learning allows for better learning and qualitatively better solutions. Specifically, we exploit the max-flow min-cut theorem to simultaneously learn these two algorithms over synthetically generated graphs, demonstrating the effectiveness of the proposed approach. We then validate the real-world utility of our dual algorithmic reasoner by deploying it on a challenging brain vessel classification task, which likely depends on the vessels' flow properties. We demonstrate a clear performance gain when using our model within such a context, and empirically show that learning the max-flow and min-cut algorithms together is critical for achieving such a result. | Published as a conference paper at ICLR 2023 DUAL ALGORITHMIC REASONING |
d248834106 | This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate visionspecific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address this issue, we propose the ViT-Adapter, which allows plain ViT to achieve comparable performance to vision-specific transformers. Specifically, the backbone in our framework is a plain ViT that can learn powerful representations from large-scale multi-modal data. When transferring to downstream tasks, a pretraining-free adapter is used to introduce the image-related inductive biases into the model, making it suitable for these tasks. We verify ViT-Adapter on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation. Notably, without using extra detection data, our ViT-Adapter-L yields state-of-the-art 60.9 box AP and 53.0 mask AP on COCO testdev. We hope that the ViT-Adapter could serve as an alternative for vision-specific transformers and facilitate future research. Code and models will be released at https://github.com/czczup/ViT-Adapter.Step1: Image Modality Pre-trainingStep2: Fine-tuning * Equal contribution. Corresponding authors. | Published as a conference paper at ICLR 2023 VISION TRANSFORMER ADAPTER FOR DENSE PREDICTIONS |
d248798499 | Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules. The implementation is available at https://github.com/sluxsr/r5 graph reasoning. * Equal contribution. | R5: RULE DISCOVERY WITH REINFORCED AND RE- CURRENT RELATIONAL REASONING |
d237364241 | We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors. A quantitative analysis of disentanglement has been based on metrics designed to compare how one variable explains each generative factor. Current metrics, however, may fail to detect entanglement that involves more than two variables, e.g., representations that duplicate and rotate generative factors in high dimensional spaces. In this work, we establish a framework to analyze information sharing in a multivariate representation with Partial Information Decomposition and propose a new disentanglement metric. This framework enables us to understand disentanglement in terms of uniqueness, redundancy, and synergy. We develop an experimental protocol to assess how increasingly entangled representations are evaluated with each metric and confirm that the proposed metric correctly responds to entanglement. Through experiments on variational autoencoders, we find that models with similar disentanglement scores have a variety of characteristics in entanglement, for each of which a distinct strategy may be required to obtain a disentangled representation. | DISENTANGLEMENT ANALYSIS WITH PARTIAL INFOR- MATION DECOMPOSITION |
d203838320 | We present a method for gating deep-learning architectures on a fine-grained level. Individual convolutional maps are turned on/off conditionally on features in the network. This method allows us to train neural networks with a large capacity, but lower inference time than the full network. To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner. We also introduce a generally applicable tool batch-shaping that matches the marginal aggregate posteriors of features in a neural network to a pre-specified prior distribution. We use this novel technique to force gates to be more conditional on the data. We present results on CIFAR-10 and ImageNet datasets for image classification and Cityscapes for semantic segmentation. Our results show that our method can slim down large architectures conditionally, such that the average computational cost on the data is on par with a smaller architecture, but with higher accuracy. In particular, our ResNet34 gated network achieves a performance of 72.55% top-1 accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for similar complexity. We also show that the resulting networks automatically learn to use more features for difficult examples and fewer features for simple examples. * Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc. | Batch-Shaped Channel Gated Networks |
d253553209 | Neural Processes (NPs) are popular methods in meta-learning that can estimate predictive uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the-art method Transformer Neural Processes (TNPs) achieve strong performance but require quadratic computation with respect to the number of context datapoints, significantly limiting its scalability. Conversely, existing sub-quadratic NP variants perform significantly worse than that of TNPs. Tackling this issue, we propose Latent Bottlenecked Attentive Neural Processes (LBANPs), a new computationally efficient sub-quadratic NP variant, that has a querying computational complexity independent of the number of context datapoints. The model encodes the context dataset into a constant number of latent vectors on which self-attention is performed. When making predictions, the model retrieves higher-order information from the context dataset via multiple cross-attention mechanisms on the latent vectors. We empirically show that LBANPs achieve results competitive with the state-of-the-art on meta-regression, image completion, and contextual multi-armed bandits. We demonstrate that LBANPs can trade-off the computational cost and performance according to the number of latent vectors. Finally, we show LBANPs can scale beyond existing attention-based NP variants to larger dataset settings. -learning surrogate models for sequential decision making. arXiv preprint arXiv: | Published as a conference paper at ICLR 2023 LATENT BOTTLENECKED ATTENTIVE NEURAL PRO- CESSES |
d235614268 | We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When multimodality is not required, our surprising finding is that we do not need probabilistic posterior predictives: deterministic models are on par, in fact they consistently (although non-significantly) outperform their probabilistic counterparts. We also found that heteroscedasticity at training time, perhaps acting as a regularizer, improves predictions at longer horizons. At the methodological side, we design metrics and an experimental protocol which can be used to evaluate the various models, predicting their asymptotic performance when using them on the control problem. Using this framework, we improve the state-of-the-art sample complexity of MBRL on Acrobot by two to four folds, using an aggressive training schedule which is outside of the hyperparameter interval usually considered. | MODEL-BASED MICRO-DATA REINFORCEMENT LEARN- ING: WHAT ARE THE CRUCIAL MODEL PROPERTIES AND WHICH MODEL TO CHOOSE? |
d9747411 | Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image classification, a question remains: have these models have solved the dataset bias problem? In general, training or fine-tuning a state-ofthe-art deep model on a new domain requires a significant amount of data, which for many applications is simply not available. Transfer of models directly to new domains without adaptation has historically led to poor recognition performance. In this paper, we pose the following question: is a single image dataset, much larger than previously explored for adaptation, comprehensive enough to learn general deep models that may be effectively applied to new image domains? In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be? We show that a generic supervised deep CNN model trained on a large dataset reduces, but does not remove, dataset bias. Furthermore, we propose several methods for adaptation with deep models that are able to operate with little (one example per category) or no labeled domain specific data. Our experiments show that adaptation of deep models on benchmark visual domain adaptation datasets can provide a significant performance boost. | One-Shot Adaptation of Supervised Deep Convolutional Models |
d29169789 | In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels using a single model. The proposed control problem contains an image restoration dynamic which is modeled by a convolutional RNN. The moving endpoint, which is essentially the terminal time of the associated dynamic, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage. * Equal contribution. arXiv:1805.07709v2 [cs.CV] 10 Oct 2018 Dynamically Unfolding Recurrent Restorer Recent years, deep learning models for image restoration tasks have significantly advanced the state-of-the-art of the field. Jain & Seung (2009) proposed a convolutional neural network (CNN) for image denoising which has better expressive power than the MRF models by Lan et al. (2006). Inspired by nonlinear diffusions, Chen & Pock (2017) designed a deep neural network for image denoising and Zhang et al. (2017a) improves the capacity by introducing a deeper neural network with residual connections. Tai et al.(2017)introduced a deep network with long term memory which was inspired by neural science. However, these models cannot gracefully handle images with varied degradation levels. Although one may train different models for images with different levels, this may limit the application of these models in practice due to lack of flexibility.Taking blind image denoising for example. Zhang et al. (2017a) designed a 20-layer neural network for the task, called DnCNN-B, which had a huge number of parameters. To reduce number of parameters, Lefkimmiatis (2017) proposed the UNLNet 5 , by unrolling a projection gradient algorithm for a constrained optimization model. However, Lefkimmiatis (2017) also observed a drop in PSNR comparing to DnCNN. Therefore, the design of a light-weighted and yet effective model for blind image denoising remains a challenge. Moreover, deep learning based models trained on simulated gaussian noise images usually fail to handle real world noise, as will be illustrated in later sections.Another example is JPEG image deblocking. JPEG is the most commonly used lossy image compression method. However, this method tend to introduce undesired artifacts as the compression rate increases. JPEG image deblocking aims to eliminate the artifacts and improve the image quality. Recently, deep learning based methods were proposed for JPEG deblocking(Dong et al., 2015;Zhang et al., 2017a;. However, most of their models are trained and evaluated on a given quality factor. Thus it would be hard for these methods to apply to Internet images, where the quality factors are usually unknown. | Dynamically Unfolding Recurrent Restorer DYNAMICALLY UNFOLDING RECURRENT RESTORER: A MOVING ENDPOINT CONTROL METHOD FOR IMAGE RESTORATION |
d262814796 | Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks. Despite its empirical success, a full theoretical understanding of BN is yet to be developed. In this work, we analyze BN through the lens of convex optimization. We introduce an analytic framework based on convex duality to obtain exact convex representations of weight-decay regularized ReLU networks with BN, which can be trained in polynomial-time. Our analyses also show that optimal layer weights can be obtained as simple closed-form formulas in the high-dimensional and/or overparameterized regimes. Furthermore, we find that Gradient Descent provides an algorithmic bias effect on the standard non-convex BN network, and we design an approach to explicitly encode this implicit regularization into the convex objective. Experiments with CIFAR image classification highlight the effectiveness of this explicit regularization for mimicking and substantially improving the performance of standard BN networks. | Published as a conference paper at ICLR 2022 DEMYSTIFYING BATCH NORMALIZATION IN RELU NETWORKS: EQUIVALENT CONVEX OPTIMIZATION MODELS AND IMPLICIT REGULARIZATION |
d15201887 | The creation of practical deep learning data-products often requires parallelization across processors and computers to make deep learning feasible on large data sets, but bottlenecks in communication bandwidth make it difficult to attain good speedups through parallelism. Here we develop and test 8-bit approximation algorithms which make better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations. We show that these approximations do not decrease predictive performance on MNIST, CIFAR10, and ImageNet for both model and data parallelism and provide a data transfer speedup of 2x relative to 32-bit parallelism. We build a predictive model for speedups based on our experimental data, verify its validity on known speedup data, and show that we can obtain a speedup of 50x and more on a system of 96 GPUs compared to a speedup of 23x for 32-bit. We compare our data types with other methods and show that 8-bit approximations achieve state-of-the-art speedups for model parallelism. Thus 8-bit approximation is an efficient method to parallelize convolutional networks on very large systems of GPUs. | 8-BIT APPROXIMATIONS FOR PARALLELISM IN DEEP LEARNING |
d225094135 | Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facilitate action-value estimation. While advances in deep learning have seamlessly driven progress in learning state representations, given the specificity of the notion of agency to RL, little attention has been paid to learning action representations. We conjecture that leveraging the combinatorial structure of multi-dimensional action spaces is a key ingredient for learning good representations of action. To test this, we set forth the action hypergraph networks framework-a class of functions for learning action representations in multi-dimensional discrete action spaces with a structural inductive bias. Using this framework we realise an agent class based on a combination with deep Q-networks, which we dub hypergraph Q-networks. We show the effectiveness of our approach on a myriad of domains: illustrative prediction problems under minimal confounding effects, Atari 2600 games, and discretised physical control benchmarks.Published as a conference paper at ICLR 2021Our results advocate for the general usefulness of leveraging the combinatorial structure of multidimensional discrete action spaces, especially in problems with larger action spaces. | Published as a conference paper at ICLR 2021 LEARNING TO REPRESENT ACTION VALUES AS A HYPERGRAPH ON THE ACTION VERTICES |
d231728364 | 3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust when objects are partially occluded or viewed from a previously unseen pose. Inspired by the robustness of generative vision models to partial occlusion, we propose to integrate deep neural networks with 3D generative representations of objects into a unified neural architecture that we term NeMo. In particular, NeMo learns a generative model of neural feature activations at each vertex on a dense 3D mesh. Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image. To avoid local optima in the reconstruction loss, we train the feature extractor to maximize the distance between the individual feature representations on the mesh using contrastive learning. Our extensive experiments on PASCAL3D+, occluded-PASCAL3D+ and ObjectNet3D show that NeMo is much more robust to partial occlusion and unseen pose compared to standard deep networks, while retaining competitive performance on regular data. Interestingly, our experiments also show that NeMo performs reasonably well even when the mesh representation only crudely approximates the true object geometry with a cuboid, hence revealing that the detailed 3D geometry is not needed for accurate 3D pose estimation. The code is publicly available at | Published as a conference paper at ICLR 2021 NEMO: NEURAL MESH MODELS OF CONTRASTIVE FEATURES FOR ROBUST 3D POSE ESTIMATION |
d246863735 | Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is undesirable because memorization violates privacy (exposing user data), degrades utility (repeated easy-to-memorize text is often low quality), and hurts fairness (some texts are memorized over others). We describe three log-linear relationships that quantify the degree to which LMs emit memorized training data. Memorization significantly grows as we increase (1) the capacity of a model, (2) the number of times an example has been duplicated, and (3) the number of tokens of context used to prompt the model. Surprisingly, we find the situation becomes more complicated when generalizing these results across model families. On the whole, we find that memorization in LMs is more prevalent than previously believed and will likely get worse as models continues to scale, at least without active mitigations. * Authors ordered alphabetically. This paper addresses both of the above open questions by comprehensively quantifying memorization across three families of neural language models and their associated datasets. We leverage access to each model's original training set to provide order-of-magnitude more precise bounds on the amount of extractable data that an adversary could recover than in prior works.We first construct a set of prompts from the model's training set. By feeding prefixes of these prompts into the trained model, we check whether the model has the ability to complete the rest of the example verbatim. This allows us to measure memorization across models, datasets, and prompts of varying sizes. We identify three properties that significantly impact memorization:1. Model scale: Within a model family, larger models memorize 2-5× more than smaller models. 2. Data duplication: Examples repeated more often are more likely to be extractable. 3. Context: It is orders of magnitude easier to extract sequences when given a longer context.Our analysis suggests that future research on neural language modeling will need to take steps to prevent future (larger) models from memorizing their training datasets. , et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021. Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006. William Fedus, Barret Zoph, and Noam Shazeer. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. arXiv preprint arXiv:2101.03961, 2021.Vitaly Feldman and Chiyuan Zhang. What neural networks memorize and why: Discovering the long tail via influence estimation. In Advances in Neural Information Processing Systems, 2020.Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. Model inversion attacks that exploit confidence information and basic countermeasures. In | Published as a conference paper at ICLR 2023 QUANTIFYING MEMORIZATION ACROSS NEURAL LANGUAGE MODELS |
d52912118 | Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of 5% − 35% in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in 124/144 (86%) of the studied settings. Selfmodulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN. | ON SELF MODULATION FOR GENERATIVE ADVER- SARIAL NETWORKS |
d256846732 | Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol , there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821(Minn et al., 2018;Yeung & Yeung, 2018). Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named SIMPLEKT. Inspired by the Rasch model in psychometrics, we explicitly model questionspecific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at https://github.com/pykt-team/pykt-toolkit 1 . * The corresponding author: Shuyan Huang. 1 We merged our model to the PYKT benchmark at https://pykt.org/. | SIMPLEKT: A SIMPLE BUT TOUGH-TO-BEAT BASE- LINE FOR KNOWLEDGE TRACING |
d16938012 | Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L 0 norm, however its optimization is NP-hard. Mixed norms, such as L 1 /L 2 measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L 1 norm. However, present algorithms designed for optimizing the mixed norm L 1 /L 2 are slow and other formulations for sparse NMF have been proposed such as those based on L 1 and L 0 norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets. | Block Coordinate Descent for Sparse NMF |
d251648059 | How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this problem is to learn low-dimensional state representations from observations, from which an effective policy can be learned. In order to boost the learning of state encoding, recent works are focused on capturing behavioral similarities between state representations or applying data augmentation on visual observations. In this paper, we propose a novel meta-learner-based framework for representation learning regarding behavioral similarities for reinforcement learning. Specifically, our framework encodes the high-dimensional observations into two decomposed embeddings regarding reward and dynamics in a Markov Decision Process (MDP). A pair of meta-learners are developed, one of which quantifies the reward similarity and the other quantifies dynamics similarity over the correspondingly decomposed embeddings. The meta-learners are self-learned to update the state embeddings by approximating two disjoint terms in on-policy bisimulation metric. To incorporate the reward and dynamics terms, we further develop a strategy to adaptively balance their impacts based on different tasks or environments. We empirically demonstrate that our proposed framework outperforms state-of-the-art baselines on several benchmarks, including conventional DM Control Suite, Distracting DM Control Suite and a self-driving task CARLA. | Published as a conference paper at ICLR 2022 LEARNING GENERALIZABLE REPRESENTATIONS FOR REINFORCEMENT LEARNING VIA ADAPTIVE META- LEARNER OF BEHAVIORAL SIMILARITIES |
d779900 | We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces decompositions built of basic signal processing operations with a learned decomposition based on statistical inference. This paper formulates the PoF model and derives a mean-field method for posterior inference and a variational EM algorithm to estimate the model's free parameters. We demonstrate PoF's potential for audio processing on a bandwidth expansion task, and show that PoF can serve as an effective unsupervised feature extractor for a speaker identification task. * This work was performed while Dawen Liang was an intern at Adobe Research. | A Generative Product-of-Filters Model of Audio |
d247627899 | Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feasible, or "afforded", at the present moment, an agent can spend more time both evaluating the utility of and acting on what matters. To this end, we present Hierarchical Affordance Learning (HAL), a method that learns a model of hierarchical affordances in order to prune impossible subtasks for more effective learning. Existing works in hierarchical reinforcement learning provide agents with structural representations of subtasks but are not affordance-aware, and by grounding our definition of hierarchical affordances in the present state, our approach is more flexible than the multitude of approaches that ground their subtask dependencies in a symbolic history. While these logic-based methods often require complete knowledge of the subtask hierarchy, our approach is able to utilize incomplete and varying symbolic specifications. Furthermore, we demonstrate that relative to non-affordance-aware methods, HAL agents are better able to efficiently learn complex tasks, navigate environment stochasticity, and acquire diverse skills in the absence of extrinsic supervision-all of which are hallmarks of human learning. 1 * Correspondence to rscostal@usc.edu 1 Code and videos of agent trajectories are available at https | Published as a conference paper at ICLR 2022 POSSIBILITY BEFORE UTILITY: LEARNING AND USING HIERARCHICAL AFFORDANCES |
d4739525 | In this paper, we propose a new feature extraction technique for program execution logs. First, we automatically extract complex patterns from a program's behavior graph. Then, we embed these patterns into a continuous space by training an autoencoder. We evaluate the proposed features on a real-world malicious software detection task. We also find that the embedding space captures interpretable structures in the space of pattern parts. | Workshop track -ICLR 2017 SEMANTIC EMBEDDINGS FOR PROGRAM BEHAVIOR PATTERNS |
d4986726 | In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying information. We address these two related issues by learning flexible representations that minimize the capability of an adversarial critic. This adversary is trying to predict the relevant sensitive variable from the representation, and so minimizing the performance of the adversary ensures there is little or no information in the representation about the sensitive variable. We demonstrate this adversarial approach on two problems: making decisions free from discrimination and removing private information from images. We formulate the adversarial model as a minimax problem, and optimize that minimax objective using a stochastic gradient alternate min-max optimizer. We demonstrate the ability to provide discriminant free representations for standard test problems, and compare with previous state of the art methods for fairness, showing statistically significant improvement across most cases. The flexibility of this method is shown via a novel problem: removing annotations from images, from separate training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model. | CENSORING REPRESENTATIONS WITH AN ADVERSARY |
d257254909 | In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. This task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely on the initialized mean pose and shape as prior estimates and parameter regression with an iterative error feedback manner. In addition, video-based approaches model the overall change over the image-level features to temporally enhance the single-frame feature, but fail to capture the rotational motion at the joint level, and cannot guarantee local temporal consistency. To address these issues, we propose a novel Transformer-based model with a design of independent tokens. First, we introduce three types of tokens independent of the image feature: joint rotation tokens, shape token, and camera token. By progressively interacting with image features through Transformer layers, these tokens learn to encode the prior knowledge of human 3D joint rotations, body shape, and position information from large-scale data, and are updated to estimate SMPL parameters conditioned on a given image. Second, benefiting from the proposed token-based representation, we further use a temporal model to focus on capturing the rotational temporal information of each joint, which is empirically conducive to preventing large jitters in local parts. Despite being conceptually simple, the proposed method attains superior performances on the 3DPW and Human3.6M datasets. Using ResNet-50 and Transformer architectures, it obtains 42.0 mm error on the PA-MPJPE metric of the challenging 3DPW, outperforming state-of-the-art counterparts by a large margin. Code will be publicly available 1 . | CAPTURING THE MOTION OF EVERY JOINT: 3D HU- MAN POSE AND SHAPE ESTIMATION WITH INDEPEN- DENT TOKENS |
d6600821 | We address a challenging fine-grain classification problem: recognizing a font style from an image of text. In this task, it is very easy to generate lots of rendered font examples but very hard to obtain real-world labeled images. This realto-synthetic domain gap caused poor generalization to new real data in previous methods (Chen et al.(2014)). In this paper, we refer to Convolutional Neural Networks, and use an adaptation technique based on a Stacked Convolutional Auto-Encoder that exploits unlabeled real-world images combined with synthetic data. The proposed method achieves an accuracy of higher than 80% (top-5) on a realworld dataset. | REAL-WORLD FONT RECOGNITION USING DEEP NET- WORK AND DOMAIN ADAPTATION |
d254877249 | Bilevel optimization plays an essential role in many machine learning tasks, ranging from hyperparameter optimization to meta-learning. Existing studies on bilevel optimization, however, focus on either centralized or synchronous distributed setting. The centralized bilevel optimization approaches require collecting a massive amount of data to a single server, which inevitably incur significant communication expenses and may give rise to data privacy risks. Synchronous distributed bilevel optimization algorithms, on the other hand, often face the straggler problem and will immediately stop working if a few workers fail to respond. As a remedy, we propose Asynchronous Distributed Bilevel Optimization (ADBO) algorithm. The proposed ADBO can tackle bilevel optimization problems with both nonconvex upper-level and lower-level objective functions, and its convergence is theoretically guaranteed. Furthermore, it is revealed through theoretical analysis that the iteration complexity of ADBO to obtain the -stationary point is upper bounded by O( 1 2 ). Thorough empirical studies on public datasets have been conducted to elucidate the effectiveness and efficiency of the proposed ADBO. | Published as a conference paper at ICLR 2023 ASYNCHRONOUS DISTRIBUTED BILEVEL OPTIMIZA- TION |
d14741151 | A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents. | Published as a conference paper at ICLR 2017 LEARNING THROUGH DIALOGUE INTERACTIONS BY ASKING QUESTIONS |
d231627799 | Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the model to make incorrect prediction whenever the trigger pattern appears during testing. In this paper, we propose a novel defense framework Neural Attention Distillation (NAD) to erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network. The teacher network can be obtained by an independent finetuning process on the same clean subset. We empirically show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase the backdoor triggers using only 5% clean training data without causing obvious performance degradation on clean examples. † Correspondence to: Xixiang Lyu (Identifying vulnerabilities in the machine learning model supply chain. IEEE Access, 7:47230-47244, 2019.Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. | Published as a conference paper at ICLR 2021 NEURAL ATTENTION DISTILLATION: ERASING BACK- DOOR TRIGGERS FROM DEEP NEURAL NETWORKS |
d7242855 | Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, we consider univariate functions on a bounded interval and require a neural network to achieve an approximation error of ε uniformly over the interval. We show that shallow networks (i.e., networks whose depth does not depend on ε) require Ω(poly(1/ε)) neurons while deep networks (i.e., networks whose depth grows with 1/ε) require O(polylog(1/ε)) neurons. We then extend these results to certain classes of important multivariate functions. Our results are derived for neural networks which use a combination of rectifier linear units (ReLUs) and binary step units, two of the most popular type of activation functions. Our analysis builds on a simple observation: the multiplication of two bits can be represented by a ReLU. | Published as a conference paper at ICLR 2017 WHY DEEP NEURAL NETWORKS FOR FUNCTION AP- PROXIMATION? |
d248376976 | In this paper, we propose CLIP-Dissect, a new technique to automatically describe the function of individual hidden neurons inside vision networks. CLIP-Dissect leverages recent advances in multimodal vision/language models to label internal neurons with open-ended concepts without the need for any labeled data or human examples. We show that CLIP-Dissect provides more accurate descriptions than existing methods for last layer neurons where the ground-truth is available as well as qualitatively good descriptions for hidden layer neurons. In addition, our method is very flexible: it is model agnostic, can easily handle new concepts and can be extended to take advantage of better multimodal models in the future. Finally CLIP-Dissect is computationally efficient and can label all neurons from five layers of ResNet-50 in just 4 minutes, which is more than 10× faster than existing methods. Our code is available at https://github.com/Trustworthy-ML-Lab/CLIPdissect. Finally, crowdsourced user study results are available at Appendix B to further support the effectiveness of our method. | CLIP-DISSECT: AUTOMATIC DESCRIPTION OF NEU- RON REPRESENTATIONS IN DEEP VISION NETWORKS |
d3633127 | We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors. With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (Im-ageNet) 1000-class image dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator. We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images. This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator. The code with Chainer (Tokui et al., 2015), generated images and pretrained models are available at https://github.com/pfnet-research/sngan_projection. | Published as a conference paper at ICLR 2018 CGANS WITH PROJECTION DISCRIMINATOR |
d255569919 | Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representations for complex scenes and tasks remains an open challenge. Towards this goal, we introduce Neural Radiance Field Codebooks (NRC), a scalable method for learning object-centric representations through novel view reconstruction. NRC learns to reconstruct scenes from novel views using a dictionary of object codes which are decoded through a volumetric renderer. This enables the discovery of reoccurring visual and geometric patterns across scenes which are transferable to downstream tasks. We show that NRC representations transfer well to object navigation in THOR, outperforming 2D and 3D representation learning methods by 3.1% success rate. We demonstrate that our approach is able to perform unsupervised segmentation for more complex synthetic (THOR) and real scenes (NYU Depth) better than prior methods (29% relative improvement). Finally, we show that NRC improves on the task of depth ordering by 5.5% accuracy in THOR.Project Website and Code | Published as a conference paper at ICLR 2023 NEURAL RADIANCE FIELD CODEBOOKS |
d253098063 | Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario, individual participants may get compromised and perform backdoor attacks by poisoning the data (or gradients). Existing work on robust aggregation and certified FL robustness does not study how hardening benign clients can affect the global model (and the malicious clients). In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting. Moreover, we propose a trigger reverse engineering based defense and show that our method can achieve robustness improvement with guarantee (i.e., reducing the attack success rate) without affecting benign accuracy. We conduct comprehensive experiments across different datasets and attack settings. Our results on nine competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor attacks. Code is available at https://github.com/KaiyuanZh/FLIP. | Published as a conference paper at ICLR 2023 FLIP: A PROVABLE DEFENSE FRAMEWORK FOR BACKDOOR MITIGATION IN FEDERATED LEARNING |
d246294582 | Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-model transferability, which mostly assume the substitute model is trained in the same domain as the target model. However, in reality, the relevant information of the deployed model is unlikely to leak. Hence, it is vital to build a more practical black-box threat model to overcome this limitation and evaluate the vulnerability of deployed models. In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks). Specifically, we leverage a generative model to learn the adversarial function for disrupting low-level features of input images. Based on this framework, we further propose two variants to narrow the gap between the source and target domains from the data and model perspectives, respectively. Extensive experiments on coarse-grained and fine-grained domains demonstrate the effectiveness of our proposed methods. Notably, our methods outperform state-of-theart approaches by up to 7.71% (towards coarse-grained domains) and 25.91% (towards fine-grained domains) on average. Our code is available at https: //github.com/Alibaba-AAIG/Beyond-ImageNet-Attack. | Published as a conference paper at ICLR 2022 BEYOND IMAGENET ATTACK: TOWARDS CRAFTING ADVERSARIAL EXAMPLES FOR BLACK-BOX DOMAINS |
d231573479 | We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods. | Published as a conference paper at ICLR 2021 EVOLVING REINFORCEMENT LEARNING ALGORITHMS |
d257038905 | Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates. However, value function approximation gets stuck in local optima and struggles to fit the actual return, limiting the variance reduction efficacy and leading policies to sub-optimal performance. This paper focuses on improving value approximation and analyzing the effects on Deep PG primitives such as value prediction, variance reduction, and correlation of gradient estimates with the true gradient. To this end, we introduce a Value Function Search that employs a population of perturbed value networks to search for a better approximation. Our framework does not require additional environment interactions, gradient computations, or ensembles, providing a computationally inexpensive approach to enhance the supervised learning task on which value networks train. Crucially, we show that improving Deep PG primitives results in improved sample efficiency and policies with higher returns using common continuous control benchmark domains. | Published as a conference paper at ICLR 2023 IMPROVING DEEP POLICY GRADIENTS WITH VALUE FUNCTION SEARCH |
d52876166 | Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across tasks, so that multi-task learning can improve generalization on individual tasks. However we lack analytic theories that can quantitatively predict how the degree of knowledge transfer depends on the relationship between the tasks. We develop an analytic theory of the nonlinear dynamics of generalization in deep linear networks, both within and across tasks. In particular, our theory provides analytic solutions to the training and testing error of deep networks as a function of training time, number of examples, network size and initialization, and the task structure and SNR. Our theory reveals that deep networks progressively learn the most important task structure first, so that generalization error at the early stopping time primarily depends on task structure and is independent of network size. This suggests any tight bound on generalization error must take into account task structure, and explains observations about real data being learned faster than random data. Intriguingly our theory also reveals the existence of a learning algorithm that proveably out-performs neural network training through gradient descent. Finally, for transfer learning, our theory reveals that knowledge transfer depends sensitively, but computably, on the SNRs and input feature alignments of pairs of tasks. * http://web.stanford.edu/~lampinen/ | AN ANALYTIC THEORY OF GENERALIZATION DYNAM- ICS AND TRANSFER LEARNING IN DEEP LINEAR NET- WORKS |
d252355108 | Inspired by Regularized Lottery Ticket Hypothesis, which states that competitive smooth (non-binary) subnetworks exist within a dense network, we propose a fewshot class-incremental learning method referred to as Soft-SubNetworks (SoftNet). Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets. The public code is available at https://github.com/ihaeyong/ SoftNet-FSCIL.Published as a conference paper at ICLR 2023 in FSCIL. Due to the small amount of training data for new tasks, the model tends to severely overfit to new classes and quickly forget old classes, deteriorating the model performance. and Tinne Tuytelaars. A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. In empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv preprint arXiv:1312.6211, 2013. | Published as a conference paper at ICLR 2023 ON THE SOFT-SUBNETWORK FOR FEW-SHOT CLASS INCREMENTAL LEARNING |
d14403330 | Part-based representations have been shown to be very useful for image classification. Learning part-based models is often viewed as a two-stage problem. First, a collection of informative parts is discovered, using heuristics that promote part distinctiveness and diversity, and then classifiers are trained on the vector of part responses. In this paper we unify the two stages and learn the image classifiers and a set of shared parts jointly. We generate an initial pool of parts by randomly sampling part candidates and selecting a good subset using 1/ 2 regularization. All steps are driven directly by the same objective namely the classification loss on a training set. This lets us do away with engineered heuristics. We also introduce the notion of negative parts, intended as parts that are negatively correlated with one or more classes. Negative parts are complementary to the parts discovered by other methods, which look only for positive correlations. | AUTOMATIC DISCOVERY AND OPTIMIZATION OF PARTS FOR IMAGE CLASSIFICATION |
d209516160 | Sequence generation models are commonly refined with reinforcement learning over user-defined metrics. However, high gradient variance hinders the practical use of this method. To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control. Due to the correlation, the number of unique rollouts is random and adaptive to model uncertainty; those rollouts naturally become baselines for each other, and hence are combined to effectively reduce gradient variance. We also demonstrate the use of correlated MC rollouts for binary-tree softmax models, which reduce the high generation cost in large vocabulary scenarios by decomposing each categorical action into a sequence of binary actions. We evaluate our methods on both neural program synthesis and image captioning. The proposed methods yield lower gradient variance and consistent improvement over related baselines. backpropagation and approximate inference in deep generative models. In ICML, pp. 1278ICML, pp. -1286ICML, pp. , 2014 Alexander M Rush, Sumit Chopra, and Jason Weston. A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685, 2015.Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, and Wonyong Sung. SVD-softmax: Fast softmax approximation on large vocabulary neural networks. | ADAPTIVE CORRELATED MONTE CARLO FOR CON- TEXTUAL CATEGORICAL SEQUENCE GENERATION |
d15075376 | Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of some animal walks from some dog walks or some cat walks, given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning. | Can recursive neural tensor networks learn logical reasoning? |
d29778779 | It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we argue that the origin of adversarial examples is primarily due to an inherent uncertainty that neural networks have about their predictions. We show that the functional form of this uncertainty is independent of architecture, dataset, and training protocol; and depends only on the statistics of the logit differences of the network, which do not change significantly during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-theart deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white and black box attacks compared to previous attempts. * Work done as a member of the Google Brain Residency program (g.co/brainresidency). | INTRIGUING PROPERTIES OF ADVERSARIAL EXAM- PLES |
d248811447 | Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level. The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios. On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations. This inspires us to propose in this paper a deep unsupervised RGB-D saliency detection approach, which requires no manual pixel-level annotation during training. It is realized by two key ingredients in our training pipeline. First, a depth-disentangled saliency update (DSU) framework is designed to automatically produce pseudo-labels with iterative follow-up refinements, which provides more trustworthy supervision signals for training the saliency network. Second, an attentive training strategy is introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting to highlight the more reliable pseudo-labels. Extensive experiments demonstrate the superior efficiency and effectiveness of our approach in tackling the challenging unsupervised RGB-D SOD scenarios. Moreover, our approach can also be adapted to work in fully-supervised situation. Empirical studies show the incorporation of our approach gives rise to notably performance improvement in existing supervised RGB-D SOD models.Published as a conference paper at ICLR 2022 SE DSR CDCP GP DES DCMC MST ACSD LHM BSCA RBD CDB Baseline 12 10 8 6 4 2 0 30 40 50 60 Inference time (s) Weighted F-measure Accuracy (%) RGB-based method RGB&D-based method Ours +37% Unlabeled/Labelded Training Data： RGB Depth GT Tradition，Baseline，OursIn this paper, unsupervised learning refers to learning without using human annotation . | Published as a conference paper at ICLR 2022 PROMOTING SALIENCY FROM DEPTH: DEEP UNSUPERVISED RGB-D SALIENCY DETECTION |
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