263 research outputs found
Kinematic Flexibility Analysis: Hydrogen Bonding Patterns Impart a Spatial Hierarchy of Protein Motion
Elastic network models (ENM) and constraint-based, topological rigidity
analysis are two distinct, coarse-grained approaches to study conformational
flexibility of macromolecules. In the two decades since their introduction,
both have contributed significantly to insights into protein molecular
mechanisms and function. However, despite a shared purpose of these approaches,
the topological nature of rigidity analysis, and thereby the absence of motion
modes, has impeded a direct comparison. Here, we present an alternative,
kinematic approach to rigidity analysis, which circumvents these drawbacks. We
introduce a novel protein hydrogen bond network spectral decomposition, which
provides an orthonormal basis for collective motions modulated by non-covalent
interactions, analogous to the eigenspectrum of normal modes, and decomposes
proteins into rigid clusters identical to those from topological rigidity. Our
kinematic flexibility analysis bridges topological rigidity theory and ENM, and
enables a detailed analysis of motion modes obtained from both approaches. Our
analysis reveals that collectivity of protein motions, reported by the Shannon
entropy, is significantly lower for rigidity theory versus normal mode
approaches. Strikingly, kinematic flexibility analysis suggests that the
hydrogen bonding network encodes a protein-fold specific, spatial hierarchy of
motions, which goes nearly undetected in ENM. This hierarchy reveals distinct
motion regimes that rationalize protein stiffness changes observed from
experiment and molecular dynamics simulations. A formal expression for changes
in free energy derived from the spectral decomposition indicates that motions
across nearly 40% of modes obey enthalpy-entropy compensation. Taken together,
our analysis suggests that hydrogen bond networks have evolved to modulate
protein structure and dynamics
On Implicit Bias in Overparameterized Bilevel Optimization
Many problems in machine learning involve bilevel optimization (BLO),
including hyperparameter optimization, meta-learning, and dataset distillation.
Bilevel problems consist of two nested sub-problems, called the outer and inner
problems, respectively. In practice, often at least one of these sub-problems
is overparameterized. In this case, there are many ways to choose among optima
that achieve equivalent objective values. Inspired by recent studies of the
implicit bias induced by optimization algorithms in single-level optimization,
we investigate the implicit bias of gradient-based algorithms for bilevel
optimization. We delineate two standard BLO methods -- cold-start and
warm-start -- and show that the converged solution or long-run behavior depends
to a large degree on these and other algorithmic choices, such as the
hypergradient approximation. We also show that the inner solutions obtained by
warm-start BLO can encode a surprising amount of information about the outer
objective, even when the outer parameters are low-dimensional. We believe that
implicit bias deserves as central a role in the study of bilevel optimization
as it has attained in the study of single-level neural net optimization.Comment: ICML 202
Interpretability at Scale: Identifying Causal Mechanisms in Alpaca
Obtaining human-interpretable explanations of large, general-purpose language
models is an urgent goal for AI safety. However, it is just as important that
our interpretability methods are faithful to the causal dynamics underlying
model behavior and able to robustly generalize to unseen inputs. Distributed
Alignment Search (DAS) is a powerful gradient descent method grounded in a
theory of causal abstraction that has uncovered perfect alignments between
interpretable symbolic algorithms and small deep learning models fine-tuned for
specific tasks. In the present paper, we scale DAS significantly by replacing
the remaining brute-force search steps with learned parameters -- an approach
we call Boundless DAS. This enables us to efficiently search for interpretable
causal structure in large language models while they follow instructions. We
apply Boundless DAS to the Alpaca model (7B parameters), which, off the shelf,
solves a simple numerical reasoning problem. With Boundless DAS, we discover
that Alpaca does this by implementing a causal model with two interpretable
boolean variables. Furthermore, we find that the alignment of neural
representations with these variables is robust to changes in inputs and
instructions. These findings mark a first step toward faithfully understanding
the inner-workings of our ever-growing and most widely deployed language
models. Our tool is extensible to larger LLMs and is released publicly at
`https://github.com/stanfordnlp/pyvene`.Comment: NeurIPS 2023 with Author Correction
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense
We aim to provide a general framework of for computational photography that
recovers the real scene from imperfect images, via the Deep Nonparametric
Convexified Filtering (DNCF). It is consists of a nonparametric deep network to
resemble the physical equations behind the image formation, such as denoising,
super-resolution, inpainting, and flash. DNCF has no parameterization dependent
on training data, therefore has a strong generalization and robustness to
adversarial image manipulation. During inference, we also encourage the network
parameters to be nonnegative and create a bi-convex function on the input and
parameters, and this adapts to second-order optimization algorithms with
insufficient running time, having 10X acceleration over Deep Image Prior. With
these tools, we empirically verify its capability to defend image
classification deep networks against adversary attack algorithms in real-time
Exploring Gender Bias in Semantic Representations for Occupational Classification in NLP: Techniques and Mitigation Strategies
Gender bias in Natural Language Processing (NLP) models is a non-trivial problem that can perpetuate and amplify existing societal biases. This thesis investigates gender bias in occupation classification and explores the effectiveness of different debiasing methods for language models to reduce the impact of bias in the model’s representations. The study employs a data-driven empirical methodology focusing heavily on experimentation and result investigation. The study uses five distinct semantic representations and models with varying levels of complexity to classify the occupation of individuals based on their biographies
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