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- Adrian Weller, Daniel P. W. Ellis, Tony Jebara
- 2009 International Conference on Machine Learning…
- 2009

Chord sequences are a compact and useful description of music, representing each beat or measure in terms of a likely distribution over individual notes without specifying the notes exactly. Transcribing music audio into chord sequences is essential for harmonic analysis, and would be an important component in content-based retrieval and indexing, but… (More)

- Adrian Weller, Justin Domke
- AISTATS
- 2016

SUMMARY We examine the effect of clamping variables for approximate inference in undirected graphical models with pairwise relationships and discrete variables. • For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can lead only to a decrease in the partition function estimate for TRW, and an… (More)

For the MIREX 2010 Audio Chord Extraction task, we submitted a total of four systems. Our base system is a trainable chord recognizer based on two-band chroma representations and using a Structured SVM classifier to replace the more familiar hidden Markov model. We submit two versions of this system, one which transposes all training data through all 12… (More)

- Adrian Weller, Tony Jebara
- UAI
- 2014

When belief propagation (BP) converges, it does so to a stationary point of the Bethe free energy F , and is often strikingly accurate. However , it may converge only to a local optimum or may not converge at all. An algorithm was recently introduced by Weller and Jebara for attractive binary pairwise MRFs which is guaranteed to return an-approximation to… (More)

- Yarin Gal, Yutian Chen, +13 authors Yan Wu
- 2016

- Adrian Weller, Tony Jebara
- NIPS
- 2014

It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper bounded by the true partition function for a binary pairwise model that is attractive. Here we provide a new, arguably simpler proof from first principles. We make use of the idea of clamping a variable to a particular value. For an attractive model, we show… (More)

- Adrian Weller, Kui Tang, Tony Jebara, David Sontag
- UAI
- 2014

Belief propagation is a remarkably effective tool for inference, even when applied to networks with cycles. It may be viewed as a way to seek the minimum of the Bethe free energy, though with no convergence guarantee in general. A variational perspective shows that, compared to exact inference, this minimization employs two forms of approximation: (i) the… (More)

- Adrian Weller, Tony Jebara
- UAI
- 2013

Finding the most likely (MAP) configuration of a Markov random field (MRF) is NP-hard in general. A promising, recent technique is to reduce the problem to finding a maximum weight stable set (MWSS) on a derived weighted graph, which if perfect, allows inference in polynomial time. We derive new results for this approach, including a general decomposition… (More)

- Adrian Weller, Tony Jebara
- AISTATS
- 2013

—Inference in general Markov random fields (MRFs) is NP-hard, though identifying the maximum a posteriori (MAP) configuration of pairwise MRFs with submodular cost functions is efficiently solvable using graph cuts. Marginal inference, however, even for this restricted class, is in #P. We prove new formulations of derivatives of the Bethe free energy,… (More)

- Adrian Weller, Mark Rowland, David Sontag
- AISTATS
- 2016

Linear programming (LP) relaxations are widely used to attempt to identify a most likely configuration of a discrete graphical model. In some cases, the LP relaxation attains an optimum ver-tex at an integral location and thus guarantees an exact solution to the original optimization problem. When this occurs, we say that the LP relaxation is tight. Here we… (More)