GenAI4ELab/papercli-papers huggingface dataset:
Approximating How Single Head Attention Learns
Authors: Charlie Victor Snell; Ruiqi Zhong; Dan Klein; Jacob Steinhardt
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Why do models often attend to salient words, and how does this evolve throughout training? We approximate model training as a two stage process: early on in training when the attention weights are uniform, the model learns to translate individual input word `i` to `o` if they co-occur frequently. Later, the model learns to attend to `i` while the correct output is o because it knows `i` translates to `o`. To formalize, we define a model property, Knowledge to Translate Individual Words (KTIW) (e.g. knowing that `i` translates to `o`), and claim that it drives the learning of the attention. This claim is supported by the fact that before the attention mechanism is learned, KTIW can be learned from word co-occurrence statistics, but not the other way around. Particularly, we can construct a training distribution that makes KTIW hard to learn, the learning of the attention fails, and the model cannot even learn the simple task of copying the input words to the output. Our approximation explains why models sometimes attend to salient words, and inspires a toy example where a multi-head attention model can overcome the above hard training distribution by improving learning dynamics rather than expressiveness. We end by discussing the limitation of our approximation framework and suggest future directions.
TuneUp: A Training Strategy for Improving Generalization of Graph Neural Networks
Authors: Weihua Hu; Kaidi Cao; Kexin Huang; Edward W Huang; Karthik Subbian; Jure Leskovec
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite many advances in Graph Neural Networks (GNNs), their training strategies simply focus on minimizing a loss over nodes in a graph. However, such simplistic training strategies may be sub-optimal as they neglect that certain nodes are much harder to make accurate predictions on than others. Here we present TuneUp, a curriculum learning strategy for better training GNNs. Crucially, TuneUp trains a GNN in two stages. The first stage aims to produce a strong base GNN. Such base GNNs tend to perform well on head nodes (nodes with large degrees) but less so on tail nodes (nodes with small degrees). So, the second stage of TuneUp specifically focuses on improving prediction on tail nodes. Concretely, TuneUp synthesizes many additional supervised tail node data by dropping edges from head nodes and reusing the supervision on the original head nodes. TuneUp then minimizes the loss over the synthetic tail nodes to finetune the base GNN. TuneUp is a general training strategy that can be used with any GNN architecture and any loss, making TuneUp applicable to a wide range of prediction tasks. Extensive evaluation of TuneUp on two GNN architectures, three types of prediction tasks, and both inductive and transductive settings shows that TuneUp significantly improves the performance of the base GNN on tail nodes, while often even improving the performance on head nodes, which together leads up to 58.5% relative improvement in GNN predictive performance. Moreover, TuneUp significantly outperforms its variants without the two-stage curriculum learning, existing graph data augmentation techniques, as well as other specialized methods for tail nodes.
Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions
Authors: Tao Li; Zhehao Huang; Qinghua Tao; Yingwen Wu; Xiaolin Huang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Stochastic gradient descent (SGD) and its variants are considered as the de-facto methods to train deep neural networks (DNNs). While recent improvements to SGD mainly focus on the descent algorithm itself, few works pay attention to utilizing the historical solutions---as an iterative method, SGD has gone through substantial explorations before convergence. Recently, an interesting attempt is stochastic weight averaging (SWA), which significantly improves the generalization by simply averaging the solutions at the tail stage of training. In this paper, we realize that the averaging coefficients could be determined in a trainable manner and propose Trainable Weight Averaging (TWA), a novel optimization method in the reduced subspace spanned by historical solutions. TWA has much greater flexibility and can be applied to the head stage of training to achieve training efficiency while preserving good generalization capability. Further, we propose a distributed training scheme to resolve the memory burden of large-scale training with efficient parallel computation. In the extensive numerical experiments, (i) TWA achieves consistent improvements over SWA with less sensitivity to learning rate; (ii) applying TWA in the head stage of training largely speeds up the convergence, resulting in over $40\%$ time saving on CIFAR and $30\%$ on ImageNet with improved generalization compared with regular training.
Does the Half Adversarial Robustness Represent the Whole? It Depends... A Theoretical Perspective of Subnetwork Robustness
Authors: Jovon Craig; Joshua Andle; Theodore Stein Nowak; Salimeh Yasaei Sekeh
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Adversarial robustness of deep neural networks has been studied extensively and can bring security against adversarial attacks/examples. However, adversarially robust training approaches require a training mechanism on the entire deep network which can come at the cost of efficiency and computational complexity such as runtime. As a pilot study, we develop in this paper a novel theoretical framework that aims to answer the question of how can we make a whole model robust to adversarial examples by making part of a model robust? Toward promoting subnetwork robustness, we propose for the first time a new concept of semirobustness, which indicates adversarial robustness of a part of the network. We provide a theoretical analysis to show that if a subnetwork is robust and highly correlated with the rest of the network, then the remaining layers are also guaranteed to be robust. To guide the empirical investigation of our theoretical findings, we implemented our method at multiple layer depths and across multiple common image classification datasets. Experiments demonstrate that our method, with sufficient dependency between subnetworks, successfully utilizes subnetwork robustness to match fully-robust models' performance across AlexNet, VGG16, and ResNet50 benchmarks, for attack types FGSM, I-FGSM, PGD, and C&W.
Self-supervised learning with rotation-invariant kernels
Authors: Léon Zheng; Gilles Puy; Elisa Riccietti; Patrick Perez; Rémi Gribonval
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learning of image representations. Besides being fully competitive with the state of the art, our method significantly reduces time and memory complexity for self-supervised training, making it implementable for very large embedding dimensions on existing devices and more easily adjustable than previous methods to settings with limited resources. Our work follows the major paradigm where the model learns to be invariant to some predefined image transformations (cropping, blurring, color jittering, etc.), while avoiding a degenerate solution by regularizing the embedding distribution. Our particular contribution is to propose a loss family promoting the embedding distribution to be close to the uniform distribution on the hypersphere, with respect to the maximum mean discrepancy pseudometric. We demonstrate that this family encompasses several regularizers of former methods, including uniformity-based and information-maximization methods, which are variants of our flexible regularization loss with different kernels. Beyond its practical consequences for state of the art self-supervised learning with limited resources, the proposed generic regularization approach opens perspectives to leverage more widely the literature on kernel methods in order to improve self-supervised learning methods.
MERMADE: $K$-shot Robust Adaptive Mechanism Design via Model-Based Meta-Learning
Authors: Arundhati Banerjee; Soham Rajesh Phade; Stefano Ermon; Stephan Zheng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Mechanism design (MD) studies how rules and rewards shape the behavior of intelligent agents, e.g., in auctions or the economy. Simulations with AI agents are powerful tools for MD, but real-world agents may behave and learn differently than simulated agents under a given mechanism. Also, the mechanism designer may not fully observe an agent's learning strategy or rewards, and executing a mechanism may be costly, e.g., enforcing a tax might require extra labor. Hence, it is key to design robust adaptive mechanisms that generalize well to agents  with unseen (learning) behavior, are few-shot adaptable, and are cost-efficient. Here, we introduce MERMADE, a model-based meta-learning framework to learn mechanisms that can quickly adapt when facing out-of-distribution agents with different learning strategies and reward functions. First, we show that meta-learning allows adapting to the theoretically known and appropriate Stackelberg equilibrium in a simple matrix game at meta-test time, with few interactions with the agent. Second, with bandit agents, we show empirically that our approach yields strong meta-test time performance against agents with various unseen explore-exploit behaviors. Finally, we outperform baselines that separately use either meta-learning or agent behavior modeling to learn a cost-effective mechanism that is $K$-shot adaptable with only partial information about the agents.
Transferring Pretrained Diffusion Probabilistic Models
Authors: Fuming You; Zhou Zhao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Diffusion Probabilistic Models (DPMs) achieve impressive performance in visual generative tasks recently. However, the success of DPMs heavily relies on large amounts of data and optimization steps, which limits the application of DPMs to small datasets and limited computational resources. In this paper, we investigate transfer learning in DPMs to leverage the DPMs pretrained on large-scale datasets for generation with limited data. Firstly, we show that previous methods like training from scratch or determining the transferable parts is not suitable for the DPM due to its U-Net based denoising architecture with the external  denoising timestep input. To address it, we present a condition-based tuning approach to take full advantages of existing pretrained models. Concretely, we obtain the semantic embeddings of condition images by the pretrained CLIP model, and then inject these semantic informations to the pretrained DPM via a ''Attention-NonLinear'' (ANL) module. The adaptation to a new task can be achieved by only tuning the ANL module inserted into the pretrained DPM hierarchically. To further enhance the diversity of generated images, we introduce a masked sampling strategy based on the condition mechanism. Extensive experiments validate the effectiveness and efficiency of our proposed tuning approach in generative task transfer and data augmentation for semi-supervised learning.
AUTOJOIN: EFFICIENT ADVERSARIAL TRAINING FOR ROBUST MANEUVERING VIA DENOISING AUTOEN- CODER AND JOINT LEARNING
Authors: Taylor Michael Villarreal; Bibek Poudel; Ryan Wickman; Yu Shen; Weizi Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: As a result of increasingly adopted machine learning algorithms and ubiquitous sensors, many ‘perception-to-control’ systems are developed and deployed. For these systems to be trustworthy, we need to improve their robustness with adversarial training being one approach. We propose a gradient-free adversarial training technique, called AutoJoin, which is a very simple yet effective and efficient approach to produce robust models for imaged-based maneuvering. Compared to other SOTA methods with testing on over 5M perturbed and clean images, AutoJoin achieves significant performance increases up to the 40% range under gradient-free perturbations while improving on clean performance up to 300%. Regarding efficiency, AutoJoin demonstrates strong advantages over other SOTA techniques by saving up to 83% time per training epoch and 90% training data. Although not the focus of AutoJoin, it even demonstrates superb ability in defending gradient-based attacks. The core idea of AutoJoin is to use a decoder attachment to the original regression model creating a denoising autoencoder within the architecture. This architecture allows the tasks ‘maneuvering’ and ‘denoising sensor input’ to be jointly learnt and reinforce each other’s performance.
UNDERSTANDING THE ROLE OF POSITIONAL ENCODINGS IN SENTENCE REPRESENTATIONS
Authors: Lihu Chen; Gael Varoquaux; Fabian M. Suchanek
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Positional encodings are used to inject word-order information into transformer-based language models. While they can significantly enhance the quality of sentence representations, their specific contribution to language models are not fully understood, especially given recent findings that building natural-language understanding from language models with positional encodings is insensitive to word order. In this work, we investigate the role of positional encodings systematically. (1) We uncover the core function of existing positional encodings is to symmetrically combine local units by identifying two common properties, Locality, and Symmetry. (2) We reveal that positional and contextual encodings play a distinct role in understanding sentences. (3) Based on these findings, we propose a simplified new method to inject positional information into such models. Empirical studies demonstrate that this method can improve the performance of the BERT-based model on 10 downstream tasks. We hope these new probing results and findings can shed light on how to design and inject positional encodings into language models.
Linearly Mapping from Image to Text Space
Authors: Jack Merullo; Louis Castricato; Carsten Eickhoff; Ellie Pavlick
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The extent to which text-only language models (LMs)  learn to represent the physical, non-linguistic world is an open question. Prior work has shown that pretrained LMs can be taught to ``understand'' visual inputs when the models' parameters are updated on image captioning tasks. We test a stronger hypothesis: that the conceptual representations learned by text-only models are functionally equivalent (up to a linear transformation) to those learned by models trained on vision tasks. Specifically, we show that the image representations from vision models can be transferred as continuous prompts to frozen LMs by training only a single linear projection. Using these to prompt the LM achieves competitive performance on captioning and visual question answering tasks compared to models that tune both the image encoder and text decoder (such as the MAGMA model). We compare three image encoders with increasing amounts of linguistic supervision seen during pretraining: BEIT (no linguistic information), NF-ResNET (lexical category information), and CLIP (full natural language descriptions). We find that all three encoders perform equally well at transferring visual property information to the language model (e.g., whether an animal is large or small), but that image encoders pretrained with linguistic supervision more saliently encode category information (e.g., distinguishing hippo vs.\ elephant) and thus perform significantly better on benchmark language-and-vision tasks. Our results indicate that LMs encode conceptual information structurally similarly to vision-based models, even those that are solely trained on images.
A simple but effective and efficient global modeling paradigm for image restoration
Authors: man zhou; Jie Huang; Jie Xiao; Hu Yu; Danfeng Hong; Chongyi Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Global modelling-based image restoration frameworks (e.g., Transformer-like architecture) has gained popularity. Despite the remarkable advancement, the success may be at the cost of model parameters and FLOPs while the intrinsic characteristics of specific task are ignored. The objective of our work is orthogonal to previous studies and we thus tailor a simple yet effective global modelling paradigm for image restoration. The key insights which motivate our study are two-fold: 1) Fourier transform is capable of disentangling image degradation and content component, acting as the image degradation prior embedded into image restoration framework; 2) Fourier domain innately embraces global property where each pixel of Fourier space is involved with all the spatial pixels. We obey the de facto global modeling rule ``spatial interaction + channel evolution" of previous studies. Differently, we customize the core designs: multi-scale Fourier period spatial modeling and Fourier channel evolution.  Equipped with above designs, our image restoration paradigm is verified on mainstream image restoration tasks including image de-raining, image enhancement, image de-hazing, and guided image super-resolution. The extensive experiments suggest that our paradigm achieves the competitive performance with fewer computational resources. Our main focus is not to beat previous frameworks but hopes to provide an alternative global modelling-based customized image restoration framework. Code will be publicly available.
Learning Soft Constraints From Constrained Expert Demonstrations
Authors: Ashish Gaurav; Kasra Rezaee; Guiliang Liu; Pascal Poupart
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize a reward function subject to some constraints, where the constraints induce behaviors that may be otherwise difficult to express with just a reward function. We consider the setting where the reward function is given, and the constraints are unknown, and propose a method that is able to recover these constraints satisfactorily from the expert data. While previous work has focused on recovering hard constraints, our method can recover cumulative soft constraints that the agent satisfies on average per episode. In IRL fashion, our method solves this problem by adjusting the constraint function iteratively through a constrained optimization procedure, until the agent behavior matches the expert behavior. We demonstrate our approach on synthetic environments, robotics environments and real world highway driving scenarios.
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence
Authors: Brando Miranda; Patrick Yu; Yu-Xiong Wang; Oluwasanmi O Koyejo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recently, it has been observed that a transfer learning solution might be all we need to solve many few-shot learning benchmarks -- thus raising important questions about when and how meta-learning algorithms should be deployed. 
In this paper, we seek to clarify these questions by 
1. proposing a novel metric -- the {\it diversity coefficient} -- to measure the diversity of tasks in a few-shot learning benchmark and 
2. by comparing MAML and transfer learning under fair conditions (same architecture, same optimizer and all models trained to convergence).
Using the diversity coefficient, we show that the popular MiniImagenet and Cifar-fs few-shot learning benchmarks have low diversity. 
This novel insight contextualizes claims that transfer learning solutions are better than meta-learned solutions in the regime of low diversity under a fair comparison. 
Specifically, we empirically find that a low diversity coefficient correlates with a high similarity between transfer learning and Model-Agnostic Meta-Learning (MAML) learned solutions in terms of accuracy at meta-test time and classification layer similarity (using feature based distance metrics like SVCCA, PWCCA, CKA, and OPD). 
To further support our claim, we find this meta-test accuracy holds even as the model size changes. 
Therefore, we conclude that in the low diversity regime, MAML and transfer learning have equivalent meta-test performance when both are compared fairly.
We also hope our work inspires more thoughtful constructions and quantitative evaluations of meta-learning benchmarks in the future.
Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models
Authors: Shuting Shen; Junwei Lu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We propose an inferential framework testing the general community combinatorial properties of the stochastic block model.  We aim to test the hypothesis on whether a certain community property is satisfied, e.g., whether a given set of nodes belong to the same community, and provide p-values for uncertainty quantification. Our framework is applicable to all symmetric community properties. To ease the challenges caused by the combinatorial nature of community properties, we develop a novel shadowing bootstrap method.  Utilizing the symmetry, our method can find a shadowing representative of the true assignment and the number of tested assignments in the alternative is largely reduced.  In theory, we introduce a combinatorial distance between two community classes and show a combinatorial-probabilistic trade-off phenomenon. Our test is honest as long as the product of the combinatorial distance between two communities and the probabilistic distance between two connection probabilities is sufficiently large.  Besides, we show that such trade-off also exists in the information-theoretic lower bound.  We also implement numerical experiments to show the validity of our method.
Denoising Diffusion Samplers
Authors: Francisco Vargas; Will Sussman Grathwohl; Arnaud Doucet
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution. Samples from the generative model are then obtained by simulating an approximation of the time-reversal of this diffusion initialized by Gaussian samples. Practically, the intractable score terms appearing in the time-reversed process are approximated using score matching techniques. We explore here a similar idea to sample approximately from unnormalized probability density functions and estimate their normalizing constants. We consider a process where the target density diffuses towards a Gaussian. Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal.  While score matching is not applicable in this context, we can leverage many of the ideas introduced in generative modeling for Monte Carlo sampling. Existing theoretical results from denoising diffusion models also provide theoretical guarantees for DDS. We discuss the connections between DDS, optimal control and Schr\"odinger bridges and finally demonstrate DDS experimentally on a variety of challenging sampling tasks.