9 research outputs found
Leveraging Task Structures for Improved Identifiability in Neural Network Representations
This work extends the theory of identifiability in supervised learning by
considering the consequences of having access to a distribution of tasks. In
such cases, we show that identifiability is achievable even in the case of
regression, extending prior work restricted to linear identifiability in the
single-task classification case. Furthermore, we show that the existence of a
task distribution which defines a conditional prior over latent factors reduces
the equivalence class for identifiability to permutations and scaling, a much
stronger and more useful result than linear identifiability. When we further
assume a causal structure over these tasks, our approach enables simple maximum
marginal likelihood optimization together with downstream applicability to
causal representation learning. Empirically, we validate that our model
outperforms more general unsupervised models in recovering canonical
representations for both synthetic and real-world molecular data.Comment: 18 pages, 4 figures, 5 tables, 1 algorith
Adversarial Infidelity Learning for Model Interpretation
Model interpretation is essential in data mining and knowledge discovery. It
can help understand the intrinsic model working mechanism and check if the
model has undesired characteristics. A popular way of performing model
interpretation is Instance-wise Feature Selection (IFS), which provides an
importance score of each feature representing the data samples to explain how
the model generates the specific output. In this paper, we propose a
Model-agnostic Effective Efficient Direct (MEED) IFS framework for model
interpretation, mitigating concerns about sanity, combinatorial shortcuts,
model identifiability, and information transmission. Also, we focus on the
following setting: using selected features to directly predict the output of
the given model, which serves as a primary evaluation metric for
model-interpretation methods. Apart from the features, we involve the output of
the given model as an additional input to learn an explainer based on more
accurate information. To learn the explainer, besides fidelity, we propose an
Adversarial Infidelity Learning (AIL) mechanism to boost the explanation
learning by screening relatively unimportant features. Through theoretical and
experimental analysis, we show that our AIL mechanism can help learn the
desired conditional distribution between selected features and targets.
Moreover, we extend our framework by integrating efficient interpretation
methods as proper priors to provide a warm start. Comprehensive empirical
evaluation results are provided by quantitative metrics and human evaluation to
demonstrate the effectiveness and superiority of our proposed method. Our code
is publicly available online at https://github.com/langlrsw/MEED.Comment: 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
(KDD '20), August 23--27, 2020, Virtual Event, US
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Large foundation models are becoming ubiquitous, but training them from
scratch is prohibitively expensive. Thus, efficiently adapting these powerful
models to downstream tasks is increasingly important. In this paper, we study a
principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream
task adaptation. Despite demonstrating good generalizability, OFT still uses a
fairly large number of trainable parameters due to the high dimensionality of
orthogonal matrices. To address this, we start by examining OFT from an
information transmission perspective, and then identify a few key desiderata
that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast
Fourier transform algorithm enables efficient information transmission, we
propose an efficient orthogonal parameterization using butterfly structures. We
apply this parameterization to OFT, creating a novel parameter-efficient
finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a
special case, BOFT introduces a generalized orthogonal finetuning framework.
Finally, we conduct an extensive empirical study of adapting large vision
transformers, large language models, and text-to-image diffusion models to
various downstream tasks in vision and language.Comment: Technical Report (33 pages, 18 figures
Interpreting Deep Learning-Based Networking Systems
While many deep learning (DL)-based networking systems have demonstrated
superior performance, the underlying Deep Neural Networks (DNNs) remain
blackboxes and stay uninterpretable for network operators. The lack of
interpretability makes DL-based networking systems prohibitive to deploy in
practice. In this paper, we propose Metis, a framework that provides
interpretability for two general categories of networking problems spanning
local and global control. Accordingly, Metis introduces two different
interpretation methods based on decision tree and hypergraph, where it converts
DNN policies to interpretable rule-based controllers and highlight critical
components based on analysis over hypergraph. We evaluate Metis over several
state-of-the-art DL-based networking systems and show that Metis provides
human-readable interpretations while preserving nearly no degradation in
performance. We further present four concrete use cases of Metis, showcasing
how Metis helps network operators to design, debug, deploy, and ad-hoc adjust
DL-based networking systems.Comment: To appear at ACM SIGCOMM 202
Towards More Robust Interpretation via Local Gradient Alignment
Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations.
To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining robust feature attributions.
However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods.
In this paper, we provide new insights by taking such normalization into account. First, we show that for every non-negative homogeneous neural network, a naive l2-robust criterion for gradients is not normalization invariant, which means that two functions with the same normalized gradient can have different values.
Second, we formulate a normalization invariant cosine distance-based criterion and derive its upper bound, which gives insight for why simply minimizing the Hessian norm at the input, as has been done in previous work, is not sufficient for attaining robust feature attribution. Finally, we propose to combine both l2 and cosine distance-based criteria as regularization terms to leverage the advantages of both in aligning the local gradient. As a result, we experimentally show that models trained with our method produce much more robust interpretations on CIFAR-10 and ImageNet-100 without significantly hurting the accuracy, compared to the recent baselines. To the best of our knowledge, this is the first work to verify the robustness of interpretation on a larger-scale dataset beyond CIFAR-10, thanks to the computational efficiency of our method